4360 - Water Research Foundation

Transcription

Acoustic Signal Processing for Pipe
Condition Assessment
Web Report #4360
Subject Area: Infrastructure
About the Water Research Foundation
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©2014 Water Research Foundation. ALL RIGHTS RESERVED.
Acoustic Signal Processing for
Pipe Condition Assessment
Prepared by:
Peter Paulson and Roberto Mascarenhas
Pure Technologies, Ltd.,1050, 340 12 Avenue SW, Calgary, AB T2R 1L5 Canada
and
Graham E.C. Bell and Brien Clark
HDR|Schiff, 431 West Baseline Road, Claremont, CA 91711
Sponsored by:
U.S. Environmental Protection Agency
26 West Martin Luther King Drive, Cincinnati, OH 45268
Water Environment Research Foundation
635 Slater’s Lane, G-110, Alexandria, VA 22314
and
Water Research Foundation
6666 West Quincy Avenue, Denver, CO 80235
Published by:
DISCLAIMER
This study was funded by the U.S. Environmental Protection Agency (EPA), the Water
Environment Research Foundation (WERF), and the Water Research Foundation (WRF) under
Cooperative Agreement No. CR-83419201. EPA, WERF, and WRF assume no responsibility for
the content of the research study reported in this publication or for the opinions or
statements of fact expressed in the report. The mention of trade names for commercial
products does not represent or imply the approval or endorsement of EPA, WERF, or WRF.
This report is presented solely for informational purposes.
Copyright © 2014
by Water Research Foundation
ALL RIGHTS RESERVED.
No part of this publication may be copied, reproduced
or otherwise utilized without permission.
Printed in the U.S.A.
CONTENTS
LIST OF FIGURES ..................................................................................................................... VII FOREWORD……... ..................................................................................................................... XI ACKNOWLEDGMENTS ......................................................................................................... XIII EXECUTIVE SUMMARY .........................................................................................................XV CHAPTER 1: BACKGROUND INFORMATION AND PROJECT MOTIVATION .................. 1 Condition Assessment vs. Condition Monitoring ............................................................... 3 PCCP Pipe Material Characteristics ................................................................................... 3 How PCCP Fails ................................................................................................................. 4 Condition Assessment of PCCP.......................................................................................... 5 Internal Visual Inspection and Aural Sounding ...................................................... 5 Impact Echo ............................................................................................................ 5 Electromagnetic Inspection ..................................................................................... 5 Acoustic Monitoring of PCCP ............................................................................................ 6 Principals and Foundation....................................................................................... 6 Methodologies and History of Use ......................................................................... 6 Problem Statement: Passive Condition Assessment of PCCP by Processing Acoustic Data
to Mine Information Condition Assessment Information from Condition
Monitoring Signals.................................................................................................. 9 Extension of PCCP Concepts to Pipe Wall Assessment for other pipe materials ............ 11 CHAPTER 2: LITERATURE SURVEY ON RESEARCH TOPICS .......................................... 13 Background on Acoustics, Sounds and Signals ................................................................ 13 Acoustic, Emission, Monitoring and Sounds of PCCP..................................................... 15 Current State of Condition Assessment for PCCP ............................................................ 16 Current State of Acoustic Signal Processing as Applied to PCCP ................................... 17 Other Candidate Techniques for Processing Acoustic Signals from PCCP Wire Breaks
............................................................................................................................... 20 Time Domain Pattern or Characteristic Analysis ............................................................. 21 Power and Energy (Amplitude) Signal Information ............................................. 21 Time Between Wire Breaks (Wire Reliability) .................................................... 21 Feature Extraction and Analysis Methods ........................................................................ 22 Fourier Transforms ............................................................................................... 22 Wavelet Transforms .............................................................................................. 23 Dissimilarities Between Fourier and Wavelet Transforms ................................... 23 Monte Carlo Techniques for Signal Processing.................................................... 24 Pipe Wall Assessment for Other Pipe Materials ............................................................... 25 Summary of the Literature ................................................................................................ 25 v
CHAPTER 3: EXPERIMENTAL SET-UPS, TESTING PROCEDURES, AND DATA MINING
REPOSITORY ............................................................................................. 27 Passive Condition Assessment of PCCP Facility ............................................................. 27 Design of the Facility of PCA-PCCP Facility ...................................................... 27 Instrumentation Description.................................................................................. 32 Test Procedures ..................................................................................................... 34 Pipe Wall Assessment DIP Facility .................................................................................. 36 Design of the Facility ............................................................................................ 36 Description of Instrumentation ............................................................................. 36 Test Procedures ..................................................................................................... 37 Existing In-Situ Monitoring Systems................................................................................ 37 Overview of Monitoring Systems ......................................................................... 37 Participant Support................................................................................................ 38 Participant Monitoring Systems ............................................................................ 39 CHAPTER 4: RESULTS AND SIGNAL ANALYSIS FOR PCA PCCP DATA ....................... 43 Presentation of Acoustic Test Data ................................................................................... 43 Observations and Analysis of PCCP Test Data .................................................... 44 Analysis Methods.............................................................................................................. 47 Short-Time Fourier Transform ............................................................................. 48 Wavelet Analysis .................................................................................................. 48 Monte Carlo Analysis ........................................................................................... 48 Application of Best Candidate Analysis Method to Real World Data ............................. 48 Selected Data ........................................................................................................ 48 Characteristics and Comparison of Results .......................................................... 49 CHAPTER 5: PIPE WALL ASSESSMENT (NON-PCCP) RESULTS ...................................... 59 Presentation and Analysis of PWA Test Data .................................................................. 59 CHAPTER 6: SUMMARY AND CONCLUSIONS OF THE RESEARCH ............................... 61 CHAPTER 7: FUTURE RESEARCH SUGGESTIONS.............................................................. 63 REFERENCES ............................................................................................................................ 65
BIBLIOGRAPHY………………………………………………………………………………..69
ABBREVIATIONS ...................................................................................................................... 71 vi
LIST OF FIGURES
1.1: PCCP failure ............................................................................................................................ 1 1.2: LCP .......................................................................................................................................... 3 1.3: ECP .......................................................................................................................................... 3 1.4: Wire breaks lead to PCCP failure ............................................................................................ 4 1.5: PCCP management cycle ......................................................................................................... 5 1.6: Live deployment of a fiber optic cable .................................................................................... 6 1.7: Wet AFO deployment insertion stack ...................................................................................... 7 1.8: How many wire breaks are too many?..................................................................................... 7 1.9: Example of PWA using a moving sensor .............................................................................. 11 2.1: Pluck of a guitar string showing characteristic attack and decay .......................................... 14 2.2: Striking of a cymbal showing characteristic attack and decay. ............................................. 14 2.3: Typical wire break ................................................................................................................. 15 2.4: Integrated power spectral density of first normalized acoustic event
days prior to failure .......................................................................................................... 18
2.5: Integrated PSD of normalized sub-event in acoustic event less than an
hour before failure............................................................................................................. 18
2.6: Aboveground PCCP instrumented with strain gages and displacement
monitoring physical scales ................................................................................................ 19 2.7: Fourier basis functions, time-frequency tiles, and coverage of the
time-frequency plane. ....................................................................................................... 24 2.8: Daubechies wavelet basis functions, time-frequency tiles, and coverage
of the time-frequency plane. ............................................................................................. 24 3.1: Engineering drawing of test set-up ........................................................................................ 28 3.2: Pipe burial site map................................................................................................................ 29 vii
3.3(a): End plate with 2-inch tap, (b): End plate with 1-inch tap ................................................. 30 3.4: Crane-lifted PCCP ................................................................................................................. 31 3.5: PCCP stake positions ............................................................................................................. 31 3.6(a): Exposed pipe ends and pits, (b): Pit window with exposed prestressing wire .................. 32 3.7(a): Fiber insertion at pipe end, (b): Fiber splicing, (c): Impact testing for AFO system ........ 32 3.8(a): Constant pressure device at pipe end, (b): Water reservoir ............................................... 33 3.9: Volume of water added vs. number of sequential wire cuts .................................................. 34 3.10: Bolt cutters used to simulate wire breaks ............................................................................ 35 3.11: Wires cut in a systematic fashion ........................................................................................ 35 3.12: Onsite workstation ............................................................................................................... 35 3.13: Orientation of the buried PCCP test facility ........................................................................ 35 3.14(a): 12-inch ductile iron pipe, (b): Buried with earth displaced from PCCP test ................... 36 3.15(a): Various joint connections, (b): Addition of up to six (6) additional clamps ................... 36 3.16: Example of test procedure ................................................................................................... 37 3.17: Excavation of Howard County's 36-inch southwestern transmission main......................... 39 3.18: Damage to WSSC's 96-inch Potomac transmission main ................................................... 40 4.1: Fiber sensor raw signal from experimental test site wire cut ................................................ 43 4.2: Fiber sensor and hydrophone data ......................................................................................... 44 4.3: Pipe yard wire cut data........................................................................................................... 45 4.4: MCUA wire cut data .............................................................................................................. 45 4.5: Pipe yard wire cut frequency ratio vs. wire cut ..................................................................... 46 4.6: MCUA wire cut frequency ratio vs. wire cut......................................................................... 46 4.7: Measured acoustic output as a function of wire cuts ............................................................. 47 4.8: RMS average acoustic power vs. remaining wires ................................................................ 47 4.9: Average RMS power from pipe yard D2 ............................................................................... 49 viii
4.10: Average RMS power from pipe yard D2 with all cuts ........................................................ 50 4.11: Average RMS power from pipe yard D3 ............................................................................. 51 4.12: RMS power from pipe yard D2 and D3 ............................................................................... 51 4.13: Average RMS power from WSSC AFO site ....................................................................... 52 4.14: Average RMS power from Ottawa AFO site....................................................................... 53 4.15: Average RMS power from San Diego AFO site ................................................................. 53 4.16: Average RMS power from Tucson AFO site ...................................................................... 54 4.17: Average RMS power from Cutzamala AFO site ................................................................. 54 4.18: HEMP analysis from pipe yard D2 ...................................................................................... 56 4.19: HEMP analysis from pipe yard D3 ...................................................................................... 56 4.20: HEMP analysis from WSSC AFO site ................................................................................ 57 4.21: HEMP analysis from Ottawa AFO site................................................................................ 57 4.22: HEMP analysis from San Diego AFO site .......................................................................... 58 4.23: HEMP analysis from Tucson AFO site ............................................................................... 58 5.1: Pipe wall assessment data with various pipe joint connections ............................................. 59 5.2: Pipe wall assessment data, illustrating effect of additional clamps ....................................... 60
ix
FOREWORD
The Water Research Foundation (Foundation) is a nonprofit corporation dedicated to the
development and implementation of scientifically sound research designed to help drinking water
utilities respond to regulatory requirements and address high-priority concerns. The Foundation’s
research agenda is developed through a process of consultation with Foundation subscribers and
other drinking water professionals. The Foundation’s Board of Trustees and other professional
volunteers help prioritize and select research projects for funding based upon current and future
industry needs, applicability, and past work. The Foundation sponsors research projects through
the Focus Area, Emerging Opportunities, and Tailored Collaboration programs, as well as various
joint research efforts with organizations such as the U.S. Environmental Protection Agency and
the U.S. Bureau of Reclamation.
This publication is a result of a research project fully funded or funded in part by
Foundation subscribers. The Foundation’s subscription program provides a cost-effective and
collaborative method for funding research in the public interest. The research investment that
underpins this report will intrinsically increase in value as the findings are applied in communities
throughout the world. Foundation research projects are managed closely from their inception to
the final report by the staff and a large cadre of volunteers who willingly contribute their time and
expertise. The Foundation provides planning, management, and technical oversight and awards
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the research.
A broad spectrum of water supply issues is addressed by the Foundation's research agenda,
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toxicology, economics, and management. The ultimate purpose of the coordinated effort is to assist
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The true benefits of the Foundation’s research are realized when the results are implemented at the
utility level. The Foundation's staff and Board of Trustees are pleased to offer this publication as
a contribution toward that end.
Denise L. Kruger
Chair, Board of Trustees
Robert C. Renner, P.E.
Executive Director
xi
ACKNOWLEDGMENTS
The authors of this report are grateful to the following water agencies for their cooperation
and participation in this project:
Arizona Public Service Company
Central Arizona Project
City of Calgary
City of London
Dallas Water Utilities
Howard County Department of Public Works
Louisville Water Company
Metropolitan Water District of Southern California
Providence Water
San Diego County Water Authority
Tarrant Regional Water District
Tucson Water
Washington Suburban Sanitary Commission
The authors would also like to thank the members of the Project Advisory Committee for
their support and assistance:
Randy Randolph, Central Arizona Project
David Hughes, American Water
Daryl Little, Bureau of Reclamation
Jeffrey Yang, U.S. Environmental Protection Agency (EPA)
Frank Blaha, WRF Project Manager
The authors of this report are indebted to the following individuals whose voluntary
assistance made the report possible:
Bethany McDonald, John Plattsmier, Cliff Moore, John Galleher, Jr., Mark Holley, Ryan
Kraayvanger, Xiangjie Kong, Ali Alavinasab, Brian Lima, Daniel Davis, Stewart Bay,
Michael Livermore, Nabil Alfagi, Mark Webb, Adam Koebel, Catalina Goez, Muthu
Chandrasekaran, Jack Elliott, Fathalla Gheryani, Hashaon Zwawa, and Nathan Faber.
xiii
EXECUTIVE SUMMARY
OBJECTIVES
This project was designed to improve acoustic signal processing for pipe condition
assessment in an experimental environment, which includes burial, pressurization, and subsequent
intentional damage to the pre-stressing wires in three specimens of prestressed concrete cylinder
pipe (PCCP).
BACKGROUND
Current acoustic fiber optic (AFO) monitoring can supply a PCCP owner with sufficient
warning to avoid a pipeline failure, but only if the information supplied by AFO is used to initiate
an emergency pipeline shutdown fairly quickly.
Unique to PCCP, individual wire breaks create an excitation in the pipe wall that may vary
in response to the remaining compression of the pipe core. In non-PCCP, the structural excitation
would require an external source acoustic pulse, causing a response indicative of relative pipe wall
stiffness. The purpose of this project is to further research acoustic signal processing to advance
the use of this AFO technology for pipe condition assessment.
APPROACH
The project steps were as follows:
1) Conducted a literature search for relevant past investigations and data mining and
processing methodologies
2) Performed a series of tests on a buried PCCP pipe to collect controlled acoustic data
as the pipe’s pre-stressing compressive load was removed
3) Evaluated data collected using advanced and emerging data mining and processing
methodologies
4) Applied data mining techniques to Pure Technologies’ acoustic fiber optic (AFO)
wire break database to identify pipes at varying distress levels
5) Validated the results using data collected from pipes of participating utilities
RESULTS/CONCLUSIONS
The experimental data was analyzed for average RMS power and peak frequency (HEMP)
analysis. While the HEMP analysis did not prove useful, the average RMS power showed promise
for correlating the signal amplitude with the number of broken wires.
Extending the research to non-PCCP ensured applicability to the broadest possible group
of WRF subscribers. A second test setup examined the correlation of acoustic signal response and
hoop stiffness of a ductile iron pipe (DIP). The results showed that areas of increased stiffness
were discernible from other areas. The ability to successfully distinguish between areas of varying
stiffness may be useful in identifying areas of uniform (i.e., general) metal loss in in-service pipe,
as these areas would appear as sections of diminished stiffness.
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APPLICATIONS/RECOMMENDATIONS
For PCCP owners, this research is retrospectively and immediately applicable to AFO
monitored water and wastewater pipes. As an optimal result, PCCP owners would extend and
improve the service life and operating performance of their critical pipeline assets, ultimately
saving significant funds that would otherwise be used in a capital replacement program.
RESEARCH PARTNERS


U.S. Environmental Protection Agency
Water Environment Research Foundation
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CHAPTER 1: BACKGROUND INFORMATION AND PROJECT
MOTIVATION
All pipelines have a finite useful life. Failure of water pipelines can be sensational. In
particular, failure of prestressed concrete cylinder pipe (PCCP) often results in the destruction of
housing, buildings and/or roads while simultaneously flooding the surrounding areas, sometimes
at a multi-million dollar repair cost to the owner. Even more expensive pipeline replacement or
rehabilitation projects costing millions or
even billions of dollars can follow
(Galleher 2008, Essamin et al. 2005).
Further, these failures can wash out
parallel sanitary sewers, which can
destroy properties and present a public
health problem due to possible
contamination of drinking water supply
(Henry et al. 2005, Ortega et al. 2005).
More difficult to quantify are the political
and societal costs that accompany being
the feature story on national news or local
newspapers (Shaver 2009, Gaewski and
Blaha 2007). The potential consequences
of a PCCP failure incentivize PCCP
owners to assess the performance, Figure 1.1: PCCP failure
condition and risk of failure of their PCCP Source: SDCWA
systems (Romer and Bell 2005, Romer et al. 2008, Bell 2001, Galleher and Stift 1998, Marshall et
al. 2005, Ojdrovic et al. 2001, Parks et al. 2001, Zarghamee et al. 1998). Figure 1.1 shows a PCCP
pipeline following rupture.
PCCP owners need to know, at any given time, whether their pipes are at the beginning,
middle or nearing the end of their useful life, and whether significant changes in pipe condition
are occurring. Pipes with wire break quantities sufficient to exceed their structural capacity under
normal operation or during surge events are near or at the end of their useful life. The obvious next
steps in the direction of a commercially available acoustic fiber optic (AFO) monitoring system
that can establish both baseline structural condition and on-going loss of core compression are:
1.
2.
3.
4.
Collection of AFO monitoring data on a buried PCCP under controlled conditions
Selection of the appropriate acoustic data analysis technique
Extraction of the key condition information from the data
Development of algorithm(s) correlating the number of pre-exiting wire breaks on a pipe
to specific acoustic characteristics detected as each wire breaks
5. Testing of the algorithm(s) on utility participant data
6. Validation on utility participant pipelines where possible
The primary goal of this project is determine the ability of recently available monitoring
technology to determine the current condition of PCCP. Every PCCP owner dreads the possibility
of a PCCP failure creating hazard, interrupting service, ballooning budgets, and focusing public
1
attention on pipeline operation and maintenance programs. Current AFO monitoring can supply a
PCCP owner with sufficient warning to avoid a pipeline failure, but only if the information
supplied by AFO is used to initiate an emergency pipeline shut down extremely quickly. In lateJune 2010, the Washington Suburban Sanitary Commission (WSSC) 96-inch Potomac
Transmission Main began experiencing wire breaks in rapid succession. Pure Technologies
quickly identified the location of the wire breaks using a preinstalled AFO monitoring system.
WSSC was notified of the rapid structural deterioration of the pipeline. WSSC initiated an
emergency shutdown of the pipeline going into the July 4th weekend, avoided failure and was
actually heralded on the local news channel for its proactive measures (Hudson 2010). Excavation
of the suspect pipe revealed physical evidence of advanced structural deterioration.
The story does not always have this happy ending. In 2006, AFO detected multiple wire
breaks on a 66-inch San Diego County Water Authority (SDCWA) PCCP pipeline that had
recently been shut down, dewatered and recommissioned in conjunction with installation of the
AFO system. Unfortunately, a lag in acoustic data analysis and reporting, and slow data
transmission combined to delay delivery of wire break events and locations to the Water Authority.
Two months after the pipeline was recommissioned, the pipeline failed catastrophically after 18
wire breaks occurred in the day preceding the failure. The Water Authority was notified of AFOdetected wire breaks near the eventual failure two to three days prior, but did not take action
because the baseline wire breaks detected by electromagnetic inspection plus the newly reported
AFO-detected wire breaks was still not sufficient to bring the pipeline near failure (Galleher et al.
2007b). However, if the AFO monitoring signals could have been quickly analyzed over the 2
month period to provide not only wire break data, but also condition data in the immediate vicinity
of the wire breaks, the Water Authority could have had more time to act.
PCCP owners know depressurizing, dewatering, refilling, and repressurizing a pipeline for
inspection or repairs causes wear and tear on the pipe. In 2006 the City of Phoenix experienced a
catastrophic failure of the 60-inch PCCP Superior Pipeline. The pipeline was repaired and put back
into service in 2007 with an AFO monitoring system installed. A phased recommissioning plan
was implemented to gradually increase pressure in the hope of avoiding unnecessary wire breaks
and structural damage. Despite these efforts wire break activity peaked on startup at relatively low
pressure of 20 pounds per square inch (psi) or less, and then again when the pressure was increased
from 70 to 90 psi, based on data provided by the City of Phoenix.
A wet-deployed AFO monitoring system may ultimately allow PCCP owners to avoid the
stress a shutdown places on a pipeline, while still providing actionable baseline condition data and
ongoing wire break locations. Combined with structural analysis, this could provide PCCP owners
a method for making repair or replace decisions.
The purpose of this project is to further research in acoustic signal processing to advance
the use of this technology for pipe condition assessment. In particular, this includes the previous
work by Bell et al. (2009) for PCCP.
The foundation of the research is in the historical practice of using internal inspection of
an empty PCCP pipe to sound, hammer or “bong” (mechanical excitation) the interior surfaces of
pipe and listening (response signal) using the human ear for “hollow”, “punky” or dull sounds.
Changes from the familiar “ringing” sound of the “good” pipe are used as the discriminator for the
signal (Gallaher and Stift 1998, Bell et al. 2009).
The concept is that the energy released from a wire break is similar to a hammer strike and
the coupling between the incompressible fluid (water) and the pipe allows the energy/signal to
2
transmit and the response to be received by hydrophones or other acoustic sensors (Konyalian
2005).
CONDITION ASSESSMENT VS. CONDITION MONITORING
Condition assessment of pipelines is critical to the long-term operational cost and safety of
aging pipelines. Knowledge of a pipe’s condition, load bearing capacity, and remaining life is the
primary goal of any strategy of a pipeline diagnostic/prognostic system. Damage in pipe will alter
the stiffness, mass, or energy dissipation properties of a system, which in turn alter the measured
response of the system.
Acoustic wave propagation has been extensively used to detect damages in pipelines. Pure
technologies using its Soundprint®, an AFO monitoring system, has successfully determined the
rate of deterioration in PCCP based on the number of wire breaks during the monitoring period.
However, acoustic signals have not been used to assess the foregoing condition of a pipeline.
Although the basis for damage detection using acoustic signals appears intuitive, its actual
application poses many significant technical challenges. The most fundamental challenge is the
fact that damage is typically a local phenomenon and may not significantly influence the global
response of a pipe.
PCCP PIPE MATERIAL CHARACTERISTICS
Figure 1.2: LCP
Source: Galleher and Stift 1998.
Figure 1.3: ECP
Source: Galleher and Stift 1998.
PCCP is a composite material of concrete and
steel. PCCP is constructed with steel bell and spigot
joint rings welded to opposite ends of a steel cylinder
which acts primarily as a water barrier. A concrete
core and high-strength steel prestressing wire
wrapped helically around the core provide the
primary structural components. Two types of PCCP
have been widely used: lined cylinder pipe (LCP) and
embedded cylinder pipe (ECP). In LCP the concrete
core is inside the steel cylinder and high-strength
steel wire is wrapped around the cylinder. See Figure
1.2.
In ECP the steel cylinder is embedded in the
concrete core and the prestressing wires are wrapped
around the concrete core. A cement-rich mortar
coating surrounds the prestressing wires, providing
an alkaline environment and protection from external
corrosion. See Figure 1.3.
LCP has been used for large diameter water
transmission and distribution mains since 1942
(AWWA 2007). ECP was developed and first
installed in 1953 (Romer et al. 2008). The diameter
ranges for LCP and ECP are 16 to 60 inches and 30
to 256 inches, respectively. AWWA standards for
design and manufacture of PCCP have primarily been
3
governed by AWWA C301 since 1952 and AWWA C304 since 1992 (Villalobos 1998).
The initial structural design requirements for the manufacturing of PCCP tended to be
conservative (AWWA 1979, Interpace 1973, and ACPA 2007) with high factors of safety.
However, as understanding of the behavior of PCCP increased, and advances were made in
material sciences, changes in the structural design of PCCP were made to reduce the cost of
manufacturing. The increase in the applied tensile strength to the wire during manufacturing in the
late 1960s and early 1970s, reduced the amount of prestressing steel wire and allowed wire of
smaller diameter, which resulted in what appeared to be a more efficient design and economical
manufacturing. After pipe from this era started experiencing a high rate of premature failures the
engineering and manufacturing standards for PCCP began to improve. The major revisions in the
standards, design, and manufacturing of the PCCP consist of changes in the maximum diameter
of the PCCP, the quality and strength of the concrete, the thickness of the steel cylinder,
prestressing wire standards, and the thickness of the mortar coating (Price, Lewis, and Erlin 1998).
HOW PCCP FAILS
All pipes exist in a causal world; that is, cause and effect are deterministic and not random.
The fact that PCCP fail structurally after decades of service implies that the applied loads
(structural demand) were beyond the design structural capacity or that the structural capacity or
demand of the pipe has changed over time. Surge events have been reported as preceding several
catastrophic failure of PCCP.
Multiple causes for PCCP failure have been reported in the literature: a high chloride
environment (Villalobos 1998), poor quality of mortar coating (Price, Lewis, and Erlin 1998), poor
quality of prestressing wire (Walsh and Hodge 1998, Knowles
1990), a corrosive environment (Galleher and Stift 1998),
inadequate thrust resistance (Ojdrovic et al. 2001), construction
damage (Parks, Drager, and Ojdrovic 2001), cracks in the cylinder
welds (Price 1990), delamination of coating (Price 1990), and
hydrogen embrittlement (Romer et al. 2008). Over time each of
these modes results in some combination of electrochemical
deterioration of the wire and cylinder, and fatigue of the pipe
structure. Hydrogen embrittlement, one of the primary failure
modes for PCCP with Class IV wire, decreases both wire ductility
and fatigue resistance (Lewis 2002). As each wire in a PCCP
breaks, the individual pipe’s strength is incrementally reduced.
PCCP failure resulting from steel cylinder corrosion is not
typical. The investigator’s experience would say less than 10% of
the PCCP failures are due to cylinder corrosion. Generally, steel
cylinder corrosion shows up as a failure mechanism when
operating pressures are well below design limits and fluctuating
groundwater environments are present in the pipe zone.
Figure 1.4: Wire breaks
Catastrophic PCCP failures are generally due to loss of
lead to PCCP failure
structural integrity due to accumulation of broken prestressing
wires, leading to loss of compression in the concrete core. Once
core compression is compromised, structural failure is imminent (Price 1990). Figure 1.4 displays
how PCCP failure is precipitated by wire breaks.
4
For this reason, all assessment or monitoring
methods try to measure wire breaks or assess concrete
core compression using various techniques for
excitation, measurement of a response and comparison
of the excitation or response to accepted norms or
calibrations. Wire break counts are then used to evaluate
the structural capacity of the pipe based on its original
design, material information and data, surge and
operating conditions, and any known modifications to
design conditions after installation. To avoid failures,
reliable wire break data must be combined with
appropriately sophisticated structural models to allow a
PCCP owner to make timely repair or replace decisions.
This PCCP management cycle is described in Figure Figure 1.5: PCCP management cycle
1.5.
CONDITION ASSESSMENT OF PCCP
Current PCCP assessment methods provide a baseline condition, in the form of wire break
quantities or locations of loss of core compression, while monitoring methods capture on-going
distress. Commercially available baseline assessment methods include electromagnetic, visual and
sounding inspections, which sometimes require dewatering or depressurizing the pipe.
Internal Visual Inspection and Aural Sounding
Visual inspections look for circumferential and longitudinal cracking. Sounding
inspections listen for large hollow areas indicating loss of core compression. Sounding is a
subjective acoustic measurement of structural condition.
Impact Echo
The impact echo (IE) test method and its use in testing concrete pipe have been described
in detail in previous publications (Sack and Olson 1994, Sack and Olson 1998, Olson et al. 1992,
Sansalone and Carino 1986). The IE method is performed on a point-by-point basis by hitting the
test surface at a given location with a small (90 gm [0.2 lb]) instrumented impulse hammer or
impactor and recording the reflected wave energy with a displacement or accelerometer receiver
mounted to or pressed against the test surface adjacent to the impact location. It provides an
indication of thickness measurement for the PCCP core and outer mortar.
Reflections from sound areas of the pipe cover a longer path and thus take a longer time to
reflect to the receiver. Over an area with an outer mortar delamination, the signals cover a shorter
path and thus reflect quicker to the receiver. Since the reflections are more easily identified in the
frequency domain, the time domain test data of the impulse hammer (if measured) and receiver
are processed by the data acquisition system for frequency domain analyses.
Electromagnetic Inspection
Electromagnetic inspections estimate the number of broken prestressing wire wraps in a
pipe section. Electromagnetic inspection indirectly measures pipe structural condition by
5
analyzing the disruption in an induced electromagnetic signal which accompanies broken
prestressing wires.
ACOUSTIC MONITORING OF PCCP
Principals and Foundation
Active, on-going distress can be detected with AFO monitoring. There are two methods
of installation. One method attaches fasteners along the length of the installed cable. This method
is used where especially large turbulence is expected. A second method does not attach the cable
at all except where it enters and exits the pipe. This second method can be performed while the
pipe is dewatered or in operation, that is, installed without dewatering or even taking the pipe out
of service. For this latter method, the provider of AFO service uses a small drogue that tows a cord
into the pipe. The drogue is captured some miles downstream and the cord is used to pull in the
fiber optic cable (see Figure 1.6). All of this is done through pressure seals at either end while the
pipe is pressurized and operating.
Figure 1.6: Live deployment of a fiber optic cable
The investigator’s experience indicates that the un-mounted systems are quieter. Contrary
to expectation, the cable does not move around much except when the flow velocities exceed about
8 ft./second. This is likely due to the drag on the cable produced by the flow keeping the cable
tensioned. The total stress on the upstream mount of the cable increases, but the cable is designed
to operate even with hundreds of pounds of force.
AFO monitoring directly measures loss of core compression by detecting and locating
individual wire breaks as they occur. The ideal condition assessment technique would establish
baseline condition while also measuring on-going distress, without dewatering the pipeline or
otherwise taking it out of service.
Methodologies and History of Use
Acoustic monitoring of PCCP is not new. Research on acoustic monitoring began in the
early 1990s by the United States Bureau of Reclamation (Travers 1994). Early work focused on
detecting large acoustic anomalies using sensors placed at some distance from the anomalies, with
suitable equipment for relatively short periods of time, typically a few months to one year.
6
The application of fiber optic cable as the
acoustic sensor has resulted in higher signal
fidelity because the fiber optic cable is acoustically
sensitive throughout its entire length. Wire breaks
cause an excitation of the pipe structure which
propagates through both the pipe structure and the
water column. The location, amplitude and
frequency characteristics of the corresponding
acoustic signal can be measured without
attenuation or dispersion of acoustic waves
propagating through the water column, an issue
which can occur with discrete acoustic monitoring
systems. AFO monitoring systems were first
installed in 2005 which required a pipeline to be
Figure 1.7: Wet AFO deployment
dewatered for installation. Recent advances in
insertion stack
AFO deployment technology have made possible
the installation of fiber optic cable using parachutes to pull the cable through the pipeline,
as depicted in Figure 1.6. Figure 1.7 depicts the AFO cable being deployed into a pipeline
through an insertion stack.
Each AFO-detected wire break is added to the total wire break estimate provided by
electromagnetic inspections to let a PCCP owner know whether the pipe is at the beginning, middle
or end of its useful life. An advanced computational model of the pipe based on its original design
and any known modifications after installation answers the question, “How Many Wire Breaks
is Too Many?” The output of such a structural model is shown in Figure 1.8. Limit states defined
by AWWA C304 are plotted to establish the onset of cracking, cylinder and wire yield, and
failure. AFO data lets the PCCP owner literally “listen” to the pipe move through these
limit states, allowing for an approach to repair and replacement.
Figure 1.8: How many wire breaks are too many?
7
Discrete or Fixed Sensor Deployment
Initial system design was based on work done by the United States Department of the
Interior, Bureau of Reclamation and included a dual hydrophone station. The original system
concept required multiple stations inserted at regular intervals over the length of the pipeline being
monitored. These locations required wet tap or access through existing valves.
An obvious advantage of this system is that single monitoring stations can be installed
fairly easily if there is access to the outside of the pipe at regular intervals. Another advantage is
the apparently low cost of isolated stations.
Location of wire break events is usually done by comparing the times of arrival of the
acoustic wave as it encounters sensors on either side of the break. This requires that at least two
sensors be within the detection range of the wire break. The range over which the Bureau was able
to detect wire breaks was sometimes thousands of feet. This was an encouraging result as the
number of stations per mile required to monitor a section of pipe might be small for large diameter
pipe.
Disadvantages of these discrete or fixed stations included the difficulty of interconnecting
sensors on the surface, protection of several different access points, damage caused by the hot taps,
and flow noise caused by the hydrophone position normal to the flow. In addition, stations may
not be suitable for smaller diameter pipelines due to attenuation of the acoustic signal over
relatively short distance. Higgins and Paulson (2006) have found a correlation between pipe
diameter vs. sensor spacing. Small diameter pipe may require sensor spacing so tight
(approximately 100 feet on center) that it would be cost prohibitive for the owner to provide the
required number of access location to the pipe to facilitate the survey. To overcome this problem,
long hydrophone arrays were developed that could overcome this shortcoming for smaller
diameter PCCP.
Inserted Sensor Array Deployment
A hydrophone array consists of a long cable with several discrete hydrophones along its
length inserted into active sewer or water PCCP pipeline at an existing valve or wet tap location.
The hydrophone array can be manufactured in various lengths depending on the project
requirements. Sensor arrays installed have ranged in length from 1500 feet to over 6000 feet from
one insertion point. Once in place, the hydrophones continuously monitor the pipe for acoustic
events that exhibit properties characteristic of prestessing wire failures. Because the sensor are
submerged directly in the flow, there is an improvement in sensitivity and fidelity in the
measurements.
Once an event is observed the data are processed by a data acquisition system and
compared to preset acoustic criteria. If the acoustic event recorded meets the established criteria,
the event is uploaded to a remote site for further evaluation by a trained technician, using
proprietary processing software.
When an event has been determined to have all the acoustic characteristics of a prestessing
wire failure, the analytical software further evaluates the signal to allow for accurate location of
the event origin. The speed of sound in water is known as well as the spacing between the
hydrophones. By comparing the arrival time between two adjacent hydrophones, the signal
processor is able to accurately determine the location of the wire break. Advantages of the
hydrophone array over the single station are many. They include:
8
1.
2.
3.
An Inserted array requires only one insertion point for up to 6000 feet of pipeline.
Inserted Arrays could be manufactured in various lengths to facilitate specific project
requirements.
The location and spacing of the hydrophones on the Inserted Array can be customized
to provide adequate acoustic coverage in various pipe sizes.
Acoustic Fiber Optic Monitoring
Acoustic monitoring of PCCP uses the energy emitted by breaking prestressing wires to
physically locate the site of deterioration or damage. When a prestressing wire breaks or even
slips, the energy released due to the break enters the incompressible fluid and propagates along the
pipeline and in the pipe wall. Similar to “active sonar”, the energy release constitutes an excitation
signal to the pipe structural system.
The function response (both pure time domain, frequency domain, along with other
transformations) is directly related to the structural integrity of the carrier pipe. By performing
detailed analysis of data collected (response functions) from discrete and continuous acoustic
monitoring arrays, the structural condition of pipe section on which the break occurs and those in
the immediate vicinity can be determined.
Pure Technologies currently provides AFO monitoring to more than 147 miles of PCCP in
the United States, Canada and Mexico, and more than 340 miles in Libya. Pure Technologies
possesses the only known database of spontaneous wire breaks in operating pipelines as detected
with distributed fiber optic sensors. Some of the pipes monitored with fiber optic sensors over past
years have been excavated and inspected, affording information about the condition of pipes in
which spontaneous wire breaks were detected. Importantly, this allows comparison of the results
in the field with the output of models and algorithms developed from the controlled, buried test
described above. The feedback loop created in this way will improve the practical applicability of
results from this work, and test the application to different pipe types and conditions.
PROBLEM STATEMENT: PASSIVE CONDITION ASSESSMENT OF PCCP BY
PROCESSING ACOUSTIC DATA TO MINE INFORMATION CONDITION
ASSESSMENT INFORMATION FROM CONDITION MONITORING SIGNALS
The work by Bell et al. (2009) and Bell and Paulson (2010) showed that some aspects of
condition can be understood by “data mining” for acoustic events.
Data mining is a branch of computer science is the process of extracting patterns from large
data sets by combining methods from statistics, transformations and, possibly, artificial
intelligence and database management. Data mining is seen as an increasingly important tool by
modern business to transform data into actionable information. Data mining is currently used in a
wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific
analysis.
The manual extraction of patterns from data has occurred for centuries. Early methods of
identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The
proliferation, ubiquity and increasing power of computer technology has increased data collection,
storage and manipulations. As data sets have grown in size and complexity, direct hands-on data
analysis has increasingly been augmented with indirect, automatic data processing. This has been
aided by other discoveries in computer science, such as neural networks, clustering, genetic
algorithms (1950s), decision trees (1960s), and support vector machines (1980s). Data mining is
9
the process of applying these methods to data with the intention of uncovering hidden patterns. It
has been used for many years by businesses, scientists and governments to sift through volumes
of data such as airline passenger trip records, census data and supermarket scanner data to produce
market research reports.
A primary reason for using data mining is to assist in the analysis of collections of
observations of behavior. Such data are vulnerable to co-linearity because of unknown
interrelations. An unavoidable fact of data mining is that the set(s) or subset(s) of data being
analyzed may not be representative of the whole domain, and therefore may not contain examples
of certain critical relationships and behaviors that exist across other parts of the domain. To address
this sort of issue, the analysis may be augmented using experiment-based and other approaches,
such as Choice Modeling for human-generated data. In these situations, inherent correlations can
be either controlled for, or removed altogether, during the construction of the experimental design.
Data mining commonly involves four classes of tasks:




Clustering – is the task of discovering groups and structures in the data that are in some
way or another "similar", without using known structures in the data.
Classification – is the task of generalizing known structure to apply to new data. For
example, an email program might attempt to classify an email as legitimate or spam.
Common algorithms include decision tree learning, nearest neighbor, naive Bayesian
classification, neural networks, and support vector machines.
Regression – attempts to find a function which models the data with the least error.
Association rule learning – searches for relationships between variables. For example, a
supermarket might gather data on customer purchasing habits. Using association rule
learning, the supermarket can determine which products are frequently bought together and
use this information for marketing purposes. This is sometimes referred to as market basket
analysis.
The final step of knowledge discovery from data is to verify the patterns produced by the
data mining algorithms occur in the wider data set. Not all patterns found by the data mining
algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in
the training set which are not present in the general data set, a phenomenon called “over-fitting.”
To overcome this, the evaluation uses a test set of data that the data mining algorithm was not
trained on. The learnt patterns are applied to this test set and the resulting output is compared to
the desired output. For example, a data mining algorithm trying to distinguish spam from
legitimate emails would be trained on a training set of sample emails. Once trained, the learnt
patterns would be applied to the test set of emails on which it had not been trained. The accuracy
of these patterns can then be measured from how many emails they correctly classify. A number
of statistical methods may be used to evaluate the algorithm, such as receiver operating
characteristic (ROC) curves.
If the learnt patterns do not meet the desired standards, then it is necessary to reevaluate
and change the preprocessing and data mining. If the learnt patterns do meet the desired standards
then the final step is to interpret the learnt patterns and turn them into knowledge.
The process of “data mining” from data intensive streams is not unique to acoustic events
in PCCP and the Principal Co-Investigators are familiar with the processes. Reid, Bell and
Edgemon (1998) used simple statistical “data mining” processing (skew and kurtosis) of
electrochemical signals to characterize and identify types of localized corrosion (stress corrosion
10
cracking, pitting, etc.) and, eventually, train neural networks for real time and post processing. Mr.
Paulson (unpublished) used similar methods in discriminating acoustic wire break signals in unbonded post-tensioning cables from normal noise resulting from construction and other activity in
a structure using artificial learning networks. Dr. Alavinasab (Tehranizadeh et al. 2003) used
similar methods to develop a competitive (unsupervised) learning neural network algorithm for
extruding the key characters of thousands of measured earthquake signals according to their
corresponding soil types. No matter the system being “mined” the processes are the same.
EXTENSION OF PCCP CONCEPTS TO PIPE WALL ASSESSMENT FOR OTHER
PIPE MATERIALS
Acoustic data collection and analysis has been used to assess the condition of non-PCCP
using both fixed (inside or outside the pipe) and moving (inside the pipe) sensors. The propagation
of low frequency acoustic waves through fluid-filled pipes is affected by the characteristics of the
wall of the pipe (Long, Cawley, and Lowe 2003; Hunaidi 2006). The process of using propagation
velocity to assess pipe wall condition is known as acoustic pipe wall assessment (PWA). PWA
using a moving receiver within the pipeline is inherently far more detailed and precise than the use
of fixed sensors at large separation distances. Using a moving sensor or sensors can reveal spatial
distribution of anomalies, gradients, amplitudes and manufacturing variances in the pipe wall. An
example of a PWA result collected in a metallic pipeline using a moving sensor (Pure
Technologies’ SmartBall®) is shown in Figure 1.9. Depending on the pipe size and flow condition,
the technology is currently able to access the pipe wall condition at intervals of approximately 3
inches. As shown in Figure 1.9, the data reveals the joints in the line in addition to three anomalies
for a section approximately 165 feet in length.
Figure 1.9: Example of PWA using a moving sensor
This technology does not measure the pipe wall thickness, but instead measures the pipe
wall hoop stiffness; more specifically the variance in the pipe wall hoop stiffness. The rationale
for this has resulted from observations which indicate burial conditions and natural variances of
pipe wall thickness from the manufacturer are never well known. However, because neither of
these conditions changes over short spatial distances, data representing the relative apparent
stiffness can yield useful data about the structural integrity of the pipeline.
11
Known limitations in measuring propagation velocity of an acoustic wave currently apply
to PWA. These issues result from the need to measure very small changes in arrival times of waves
that exhibit very slow slew rates due to their low frequency. Typical frequencies used are a few
hundred Hertz or less, while resolution of 20–30 microseconds is desired to accurately measure
the variations in local hoop stiffness of the pipes through which a sensor is moving.
12
CHAPTER 2: LITERATURE SURVEY ON RESEARCH TOPICS
BACKGROUND ON ACOUSTICS, SOUNDS AND SIGNALS
Acoustic signals associated with wire breaks have been used to assess PCCP integrity for
more than 20 years. The focus of this research is to characterize the sounds and signals which
result from the energy release from wire breaks events (Bell et al. 2009). Acoustic signals, sounds
of wire breaks, in solids (PCCP pipe walls) are comprised of both compression and shear waves.
Only compressive waves exist in fluids, because they cannot sustain shear stress. Compressive
waves are related to compressibility and density of the fluid or solid. Shear waves only occur in
solids and are related to material stiffness, compressibility and density (Rossing et al. 2003). For
PCCP, compressibility and density of the material does not change, but apparent stiffness changes
as wires break, prestress in the concrete core is lost and delamination of exterior mortar occurs.
Analysis of measured acoustic signals from PCCP wire breaks basically comes down to
processing the digital signals so that identifying, characterizing and discriminating between these
signals is possible. Understanding and characterizing sounds is an integral part of this research.
Sounds may generally be characterized by pitch (frequency), loudness (amplitude) and quality or
timbre. Timbre allows one to distinguish between sounds with the same pitch and loudness. Timbre
is mainly determined by the harmonic (mix of frequencies) and dynamic (changes in amplitude
and frequency) characteristics of the sound (Rossing et al. 2003). The primary contributors to
timbre or quality are harmonic content, vibrato, attack and decay (Rossing et al. 2003). Harmonic
content is the number and relative intensity of upper harmonics in the sound and associated
overtones. Harmonics and overtones can be analyzed and characterized by transformations from
the time domain to the frequency domain (Rossing et al. 2003). This was the focus of Bell et al.
(2009).
The ordinary definition of vibrato is "periodic changes in the pitch of the tone," and the
term tremolo is used to indicate periodic changes in the amplitude or loudness of the tone. So
vibrato could be called FM (frequency modulation) and tremolo could be called AM (amplitude
modulation) of the tone. Actually, in the voice or the sound of a musical instrument both are usually
present to some extent. Because the acoustic events from wire breaks on PCCP are discreet and
not sustained, vibrato is not considered an important characteristic of these acoustic events
(Rossing et al. 2003).
Figure 2.1 shows the attack and decay of a plucked guitar string. The plucking action gives
it a sudden attack characterized by a rapid rise to its peak amplitude. The decay is long and gradual
by comparison. The ear is sensitive to these attack and decay rates and may be able to use them to
identify the instrument producing the sound (Rossing et al. 2003).
13
Figure 2.1: Pluck of a guitar string showing characteristic attack and decay
Figure 2.2 shows the sound envelope of striking a cymbal with a stick. The attack is almost
instantaneous, but the decay envelope is very long. The time period shown is about half a second.
The interval shown with the guitar string above is also about half a second, but since its frequency
is much lower, you can resolve the individual periods in that sound envelope. Because of the high
frequencies in the cymbal strike, the individual periods cannot be discerned within the acoustic
signal. This type of signal is most similar in shape to PCCP wire breaks as depicted in Figure 2.3
(Rossing et al. 2003).
Figure 2.2: Striking of a cymbal showing characteristic attack and decay.
14
The concepts of timbre, pitch and loudness could be useful in the analysis data since they
are similar to the processing that the human brain conducts when analyzing sounds.
ACOUSTIC, EMISSION, MONITORING AND SOUNDS OF PCCP
The use of acoustic monitoring of PCCP is not new (Worthington 1998, Paulson 1998a,
Paulson 1998b). Research on acoustic monitoring began in early 1990’s by the Bureau of
Reclamation (Travers 1994, Worthington 1992). Results up to this point have focused on
“listening” for large distinctive acoustic anomalies or events using sensors placed at some distance
from the anomalies, with suitable equipment for relatively short periods of time, typically a few
months to one year (Holley and Buchanan 1998). When wire break activity was detected, in most
cases, only a few acoustic events were recorded due to the limited installation time. Nonetheless,
valuable information was obtained from these installations (Higgins 2004).
The advent of fiber-optic cable (FOC) as an acoustic sensing element for acoustic
monitoring of PCCP results in higher signal fidelity because FOC is acoustically active along its
entire length (Higgins and Paulson 2006, Essamin and Holley 2004, Lenghi et al. 2008). A typical
acoustic event is shown in Figure 2.3. The location, amplitude and frequency characteristics of the
acoustic events (AE) and the acoustic response of the structure transmitted through the water
column are recorded without the attenuation which can occur with discrete acoustic sensing
systems. These improvements in technology have lead to longer monitoring times and the
recording of literally thousands of wire break acoustic events with very high fidelity from pipes
with a wide variety of conditions (Bell and Paulson 2010).
Figure 2.3: Typical wire break
Source: Bell and Paulson 2010
The collection of acoustic monitoring signals and their relation to pipe condition has always
been an issue. In its simplest form, acoustic signals count wires that break while you are listening,
but cannot tell you how many wires were broken before you started listening. In short, acoustic
monitoring can give you a rate of wire breaks, but not the integrated amount of broken wires. In
addition, the location of wire breaks can be estimated to within about one pipe diameter using
acoustic triangulation from hydrophones or fiber optic cable installation and the time of wire
breaks and time between wire breaks can be very accurately recorded. This level of information
15
allows correlation of wire break events with operational or external changes and permits causation
to be investigated. However, as discussed above, the condition (structural integrity and
serviceability of the pipe) are related to the integrity/condition and number of intact prestressing
wires. Wrigglesworth and Higgins (2010) found that by monitoring the rate and change in rate of
wire breaks using acoustic monitoring, catastrophic failures could be avoided. Stroebele et al.
(2010) found that depressurization and repressurization of PCCP for shut down produced wire
break acoustic events.
The ability of acoustic emission and monitoring to distinguish between wire breaks, wire
slips, mortar delaminations and concrete core cracking which also occur as the pipe condition
deteriorates was uncertain from the beginning. Bell and Paulson (2010) used empty, dry above
grade lined cylinder PCCP to collect data on lined cylinder pipe and recorded acoustic equivalent
data associated with prestressing wire being cut and with relaxation (wire slips, mortar
delaminations and concrete core cracking) over time. They concluded that wire breaks were
distinguishable from wire slips, mortar delaminations and concrete core cracking based on
amplitude. The amount of energy released from the breaking of a prestressing wire is many orders
of magnitude greater than that associated with wire slips, mortar delaminations and concrete core
cracking. This makes sense because the tensile strength of the wires is at least three orders of
magnitude greater that tensile strength of concrete and interfacial shear strength of smooth steel
on concrete. In short, wire breaks are unique, identifiable and distinguishable based on acoustic
characteristic (pitch and loudness or amplitude) from other deterioration events for PCCP (Bell
and Paulson 2010).
CURRENT STATE OF CONDITION ASSESSMENT FOR PCCP
Current PCCP assessment methods provide a baseline condition, in the form of wire break
quantities or locations of loss of core compression, while monitoring methods capture on-going
distress. Commercially available baseline assessment methods include electromagnetic, visual and
sounding inspections, which sometimes require dewatering or depressurizing the pipe.
Electromagnetic inspections estimate the number of broken prestressing wire wraps in a pipe
section, while visual and sounding inspections look and listen for circumferential cracking and
large hollow areas indicating loss of core compression (Gallaher and Stift 1998). Electromagnetic
inspection indirectly measures pipe structural condition by analyzing the disruption in an induced
electromagnetic signal which accompanies broken prestressing wires, while sounding is a
subjective acoustic measurement of structural condition. Active, on-going distress can be detected
with AFO monitoring which can typically be installed without dewatering or even taking the pipe
out of service. AFO monitoring directly measures loss of core compression by detecting and
locating individual wire breaks as they occur. The ideal condition assessment technique would
establish baseline condition while also measuring on-going distress, without dewatering the
pipeline or otherwise taking it out of service.
In contrast to acoustic monitoring, electromagnetic inspections estimate the accumulated
damage at any point in time on pipe segment. Periodic electromagnetic inspections could provide
estimates of the number of broken wires and approximate location on a pipe segment, but the rate
of wire breaks could only be inferred by consecutive electromagnetic inspections and causation
cannot be inferred.
Whether the technology is electromagnetic (Mergelas and Kong 2001) or physical
(Gallaher and Stift 1998) or a combination of techniques (Fitamant et al. 2004), condition
assessment for PCCP fundamentally comes down to correlating changes in an excitation signal
16
and a measured response signal. For electromagnetic condition assessment, changes in amplitude
and phase of essentially radio signals infer prestressing wire continuity and thereby indirectly the
structural condition of the pipe (Fitamant et al. 2004). For physical sounding, the changes in sound
measured using the human ear by physical excitation by striking the internal wall of the pipe infer
the condition of the pipe wall. The relationship and characteristics of the response are compared
with the excitation. The signal changes can then be correlated with the condition or changes in the
condition of the system.
CURRENT STATE OF ACOUSTIC SIGNAL PROCESSING AS APPLIED TO PCCP
This research focuses on using the energy released by the wire break as the acoustic
excitation signal and measure acoustic signals to assess the condition of the pipe wall. Bell and
Paulson (2010) showed that some aspects of condition can be understood by data mining. Bell et
al. (2009) and Bell and Paulson (2010) looked at simple frequency domain transformations and
statistical methods of “characterizing” the data and looking for trends. The previous work suffered
from two distinct limitations. Bell et al. could not verify or correlate the changes in mathematical
results with actual changes in structural condition and changes were not traceable to unique pipes.
Other methods of data processing or, more likely, combinations of methods may give more insight
into the condition assessment.
Bell and Paulson (2010) indicates it may be possible to harvest condition information from
acoustic data. In this work, acoustic emission data was collected and analyzed for a 72-inch San
Diego County Water Authority PCCP pipeline which failed catastrophically while being
monitored with AFO. The acoustic data analyzed in the time domain and other transformed
domains as the intervals between wire breaks decreased and the pipe ruptured. Initially, the time
between wire breaks was days, then hours, then minutes. The work supported the conclusion that
redistribution and increases in local stresses occur as wires break and other wires in the vicinity
carry the additional load. As the stresses increase locally, wires break more frequently and the pipe
“unzips” with the time between wire break acoustic events decreasing as the system (pipe)
becomes less restrained and more unstable.
Bell et al. (2009) also examined time domain plots, transformed to the power spectral
density (PSD) frequency domain using Fourier Transforms and integrated, for two specific
normalized acoustic events associated with wire breaks: one just minutes before failure (Figure
2.5) and one several days before failure (Figure 2.4). The differing PSD frequency characteristics
led Bell et al. (2009) to the conclusion that the dominant frequencies for wire breaks could be
related to the overall pipe condition.
17
Figure 2.4: Integrated power spectral density of first normalized acoustic event days prior
to failure
Source: Bell et al. 2009
Figure 2.5: Integrated PSD of normalized sub-event in acoustic event less than an hour
before failure
Source: Bell et al. 2009
Subsequent work (Bell and Paulson 2010) studied wire breaks, wire slips and
delaminations in PCCP. Above-grade 42-inch sections of PCCP were instrumented and wires
systematically cut while acoustic and mechanical distortion data were collected (Figure 2.6).
Instantaneous changes along with relatively long-term relaxation of the pipe were monitored.
Acoustic signals were correlated to mechanical measurements and observations of wire breaks,
wire slips and delaminations of the mortar coating.
18
Bell and Paulson (2010) clearly demonstrated how the delamination from a wire break
affected the signal from an adjacent wire break. A wire break adjacent to an area where
delamination has occurred would have a larger redevelopment length than would otherwise have
been the case. In that case, the total energy released by the new wire break will typically be larger
and the energy spectrum will shift downwards in frequency. As the delaminated area enlarges with
additional wire breaks, the total energy released by each wire break will increase by an order or
more.
For multiple wire breaks in an area, Bell and Paulson (2010) also demonstrated the signal
process distinction between a wire break and the “relaxation” of a wire that has already broken.
There is virtually no acoustic energy produced by relaxation of the wire after it has broken. This
has been confirmed by empirical studies by the investigators completed over protracted periods of
time. Delaminations are sometimes detected but differ greatly in their acoustic character from wire
breaks and the distinction is easy to make.
Acoustic signal processing can distinguish multiple wire breaks within a short time span in
a given pipe area. The investigator’s experience (Bell and Paulson 2012) indicates that even in
instances where one wire break (apparently) causes adjacent wires to break, the acoustic signals
do reveal the number of wires that fail, even when the occurrence is within a few milliseconds.
The sampling rate of typical equipment is such that in order for events to be “simultaneous” they
would have to occur within 15 microseconds of each other. The spatial resolution of the signals is
such that events would have to be within one meter of each other. Given the causal rather than
random nature of wire breaks, the probability of apparently simultaneous while spatially similar
events occurring is small. The only case when this might occur would be during a catastrophic
failure (short time scale, large length scale).
Bell and Paulson (2010) concluded
that wire breaks were associated with large
amplitude in the time domain and relatively
flat
frequency
distribution,
whereas
delaminations were associated with lower
amplitude and dominant frequencies less than
2 kilohertz (kHz). The difference between the
reported dominant frequencies in 72-inch
PCCP under operational pressure and 42-inch
PCCP without internal pressure indicates that
the acoustic energy release from a wire break
event could be a function of pipe type,
diameter, thickness, and class of prestressing
wires, and internal and external loads on the
Figure 2.6: Aboveground PCCP instrumented pipe.
with strain gages and displacement monitoring
The work by Bell et al. (2009) and
physical scales
Bell and Paulson (2010) showed that some
aspects of condition can be understood by
“data mining” for acoustic events. The process has been shown to be feasible, but more work
needs to be done to make the observation useful. In addition, Bell et al. (2009) and Bell and
Paulson (2010) looked at simple frequency domain transformations and statistical methods of
“characterizing” the data and looking for trends which may be related to the structural condition
of PCCP (e.g. broken prestressing wires, loss of concrete compression or mortar delaminations).
19
Other methods of data processing or, more likely, combinations of methods will give more insight
into the condition availability to the project of the unpublished details of the prior research,
including the investigators’ insight into improvements into testing apparatus, acoustic analytical
methods, and unpublished assessments that failed to produce results.
OTHER CANDIDATE TECHNIQUES FOR PROCESSING ACOUSTIC SIGNALS
FROM PCCP WIRE BREAKS
The literature of signal processing is very mature and robust compared to the techniques
originally applied by Bell and Paulson (2010). Signal processing is focused in the electrical
engineering field and many of them for condition monitoring of rotating and other mechanical
equipment (Grimmelius et al. 1999). From a fundamentals standpoint, there are three different
condition monitoring techniques: first principles, feature extraction, and neural networks
(Grimmelius et al. 1999). Acoustic signals can be processed in much the same way (Rossing et
al. 2003).
A first principles method uses mathematical simulation models based on first principal
physics to predict the behavior of machinery, both for healthy and faulty conditions. One of the
main characteristics of these simulation models is the required high level of knowledge of those
processes. They require extensive knowledge of the “first principles,” such as conservation laws,
and of constitutional laws, such as the properties of matter. Measured data is required for tuning,
validation, and verification (Grimmelius et al. 1999). The first principles approach is probably not
the most viable method for PCCP wire break analysis at this time, due to the non-uniformity of the
manufacturing process, the heterogeneity of the PCCP composite structure and complexity of the
applied loads.
Feature extraction and pattern recognition algorithms are used for analyzing signals and
for classifying (parts of the) signals into classes. The classification is done by matching (part of)
the signal with a set of reference signals. The sensor signal will be classified as a member of the
class that corresponds with the best matching reference signal. The isolation of parts of the signal
those are unique for the classes’ results in a better control of the classification problem. In this
way, the influence of fluctuations in the sensor signal which are caused by instabilities and noise
will be reduced to a minimum (Grimmelius et al. 1999). The process of isolating those parts of
the sensor signal is called the feature extraction process, while the matching process is known as
pattern recognition. This is the first step in analysis, particularly when limited data with known
excitation/response information is available and will be the at least the initial focus of the efforts
on the project.
Neural network technology is used to recognize and classify complex fault patterns without
much knowledge about the process, the signals, or the fault patterns themselves. A neural network
consists of many simple neurons which are connected with each other. The behavior of the network
is determined by the (adjustable) weights that are associated with each connection. The values of
these weights are determined during the training session. During this session, examples of the
different situations (input patterns with corresponding output classifications) are presented to the
neural network. Neural networks tend to be very robust to noise in the signals (Sarle 1998).
Application of neural network technology requires a large training data set, covering all classes of
conditions that are required to be detected. The results of a neural network are only valid within
the range of this training data set. Reid et al. (1998) trained neural networks to identify
electrochemical noise signals from localized and uniform corrosion phenomenon. For this
application, a large number of well characterized data sets were available to train the network
20
before using the neural network to analyze real time datasets. On the other hand, the ability of
neural networks to “train” themselves sometimes allows neural networks to find a solution where
other methods fail (Grimmelius et al. 1999). Once a better understanding of signals and system
responses are developed, neural networks may ultimately be utilized, but for this research, since
insufficient quality excitation/response data exists to train the a neural network, neural networks
will have to be the a future objective.
TIME DOMAIN PATTERN OR CHARACTERISTIC ANALYSIS
Power and Energy (Amplitude) Signal Information
The energy of an acoustic signal is proportional to the square of the amplitude of the signal.
When a wire breaks the energy release is related to the ‘stress state’ which exists in the wire at the
location of the break. Since wire breaks result in changes in the local stress state of the pipe,
analysis of amplitude and energy is related to the stress state and ultimately the condition of the
pipe at the location where the wire breaks. Energy analysis, spectral and otherwise, is most
applicable to systems that have finite total energy (e.g. pulse like signals such as what occurs when
a wire breaks). Amplitude and energy have been used by analysts in many forms to discriminate
and validate acoustic signals from PCCP pipe. Bell and Paulson (2010) were able to show that
energy/amplitude could be used to discriminate between wire breaks and mortar
cracking/delamination events.
Power (i.e. energy per unit time) characterization of acoustic signals has been applied in
many systems and is similar to energy characterization; power characterization is most appropriate
for stationary processes.
For continuous signals that describe stationary physical processes, it makes more sense to
define a power spectral density (PSD), which describes how the power of a signal or time series is
distributed over the different frequencies. The above definition of energy spectral density is most
suitable for transients; that is, pulse-like signals, for which the Fourier transforms of the signals
exist, is most suitable for the purposes investigated here.
Time Between Wire Breaks (Wire Reliability)
Although not investigated in this work, wire reliability is worth discussion. Reliability
theory is a scientific approach aimed to gain theoretical insights into mechanisms of survival
patterns by applying a general theory of systems failure. Basically, if we assume that each wire is
part of a population of wires which will eventually fail (failure = unable to perform its intended
function), that the wires in the population were manufactured with the same processes and flaws
and the wire in the population are subjected to the same environment (on average), then we can
use reliability theory to predict the trend in wire breaks. This is very similar to the process that
Romer et al. (2008) developed for PCCP pipe failures and others have applied to various pipe
break models such as KANEW. The process would be similar. Advantages of the technique are
that it does not require any mechanistic information about the processes and works simply from
wire break data from the actual system, so it is internally self-consistent and deterministic. The
disadvantages are that in order to be statistically significant, dozens of wire breaks are needed to
establish models, the assumption that the influencing factors are stationary and the inability of the
reliability function to determine the structural condition of a pipe or which specific pipe segment
is at risk. Wire reliability modeling may be useful in some specific large populations of PCCP
21
where large numbers of wire breaks can be tolerated and used to analyze all wire breaks and pipe
designs and operating characteristics are sufficiently similar such that the stationary assumption is
valid, but it was not investigated in this work.
FEATURE EXTRACTION AND ANALYSIS METHODS
Fourier Transforms
Fourier's representation of functions as a superposition of sine and cosine function has
become ubiquitous for both the analytic and numerical solution of differential equations and for
the analysis and treatment of communication signals.
The Fourier transform's utility lies in its ability to analyze a signal in the time domain for
its frequency content. The transform works by first translating a function in the time domain into
a function in the frequency domain. The signal can then be analyzed for its frequency content
because the Fourier coefficients of the transformed function represent the contribution of each sine
and cosine function at each frequency. An inverse Fourier transform transforms data from the
frequency domain into the time domain (Lathi 2000).
Discrete Fourier Transforms
The discrete Fourier transform (DFT) estimates the Fourier transform of a function from a
finite number of its sampled points. The sampled points are supposed to be typical of what the
signal looks like at all other times. This was the methodology of Bell and Paulson (2010). The
DFT has symmetry properties almost exactly the same as the continuous Fourier transform. In
addition, the formula for the inverse discrete Fourier transform is easily calculated using the one
for the discrete Fourier transform because the two formulas are almost identical (Lathi 2000).
Windowed Fourier Transforms
If f(t) is a nonperiodic signal, the summation of the periodic functions, sine and cosine,
does not accurately represent the signal. You could artificially extend the signal to make it periodic
but it would require additional continuity at the endpoints. The windowed Fourier transform
(WFT) is one solution to the problem of better representing the nonperiodic signal. The WFT can
be used to give information about signals simultaneously in the time domain and in the frequency
domain (Lathi 2000).
With the WFT, the input signal data are chopped up into sections, and each section is
analyzed for its frequency content separately. If the signal has sharp transitions (Figure 2.3), WFT
splits the input data so that the sections converge to zero at the endpoints. This windowing is
accomplished via a weight function that places less emphasis near the interval's endpoints than in
the middle. The effect of the window is to localize the signal in time (Lathi 2000).
Fast Fourier Transforms
To approximate a function by samples, and to approximate the Fourier integral by the
discrete Fourier transform, requires applying a matrix whose order is the number sample points n.
Since multiplying an n x n matrix by a vector costs on the order of n3 arithmetic operations, the
problem gets quickly worse as the number of sample points increases. However, if the samples are
22
uniformly spaced, then the Fourier matrix can be factored into a product of just a few sparse
matrices, and the resulting factors can be applied to a vector in a total of order nlog2n arithmetic
operations. This is the so-called fast Fourier transform (FFT).
Wavelet Transforms
Wavelets are mathematical functions that cut up data into different frequency components,
and then study each component with a resolution matched to its own scale. They have advantages
over traditional Fourier methods in analyzing physical situations where the signal contains
discontinuities and sharp spikes (Figure 2.3). Wavelets were developed independently in the fields
of mathematics, quantum physics, electrical engineering, and seismic geology. Interchanges
between these fields during the last ten years have led to many new wavelet applications such as
image compression, turbulence, human vision, radar, and earthquake prediction.
The FFT and the discrete wavelet transform (DWT) are both linear operations that generate
a data structure that contains log2n segments of various lengths, usually filling and transforming
it into a different data vector of length 2n.
The mathematical properties of the matrices involved in the transforms are similar as well.
The inverse transform matrix for both the FFT and the DWT is the transpose of the original. As a
result, both transforms can be viewed as a rotation in function space to a different domain. For the
FFT, this new domain contains basis functions that are sines and cosines. For the wavelet
transform, this new domain contains more complicated basis functions called wavelets, mother
wavelets, or analyzing wavelets (Mallat 2009).
Both transforms have another similarity. The basis functions are localized in frequency,
making mathematical tools such as power spectra (how much power is contained in a frequency
interval) and scalegrams useful at picking out frequencies and calculating power distributions.
Dissimilarities between Fourier and Wavelet Transforms
The most interesting dissimilarity between these two kinds of transforms is that individual
wavelet functions are localized in space. Fourier sine and cosine functions are not. This
localization feature, along with wavelets' localization of frequency, makes many functions and
operators using wavelets "sparse" when transformed into the wavelet domain. This sparseness, in
turn, results in a number of useful applications such as data compression, detecting features in
images, and removing noise from time series.
One way to see the time-frequency resolution differences between the Fourier transform
and the wavelet transform is to look at the basis function coverage of the time-frequency plane.
Figure 2.7 shows a windowed Fourier transform, where the window is simply a square wave. The
square wave window truncates the sine or cosine function to fit a window of a particular width.
Because a single window is used for all frequencies in the WFT, the resolution of the analysis is
the same at all locations in the time-frequency plane.
23
Figure 2.7: Fourier basis functions, time-frequency tiles, and coverage of the timefrequency plane.
Source: based on Graps 1995
An advantage of wavelet transforms is that the windows vary. In order to isolate signal
discontinuities, one would like to have some very short basis functions. At the same time, in order
to obtain detailed frequency analysis, one would like to have some very long basis functions. A
way to achieve this is to have short high-frequency basis functions and long low-frequency ones.
This happy medium is exactly what you get with wavelet transforms. Figure 2.8 shows the
coverage in the time-frequency plane with one wavelet function, the Daubechies wavelet.
Figure 2.8: Daubechies wavelet basis functions, time-frequency tiles, and coverage of the
time-frequency plane.
Source: based on Graps 1995.
One thing to remember is that wavelet transforms do not have a single set of basis functions
like the Fourier transform, which utilizes just the sine and cosine functions. Instead, wavelet
transforms have an infinite set of possible basis functions. Thus, wavelet analysis provides
immediate access to information that can be obscured by other time-frequency methods such as
Fourier analysis.
Monte Carlo Techniques for Signal Processing
In general, Monte Carlo methods are used in mathematics to solve various problems by
generating suitable random numbers and observing that fraction of the numbers which obeys some
property or properties. The method is useful for obtaining numerical solutions to problems which
24
are too complicated to solve analytically. The most common application of the Monte Carlo
method is Monte Carlo integration.
A large number of statistical signal processing applications including filtering, estimation,
and detection require evaluation of integrals, optimization and simulation of stochastic systems.
These methods have not only played a prominent role in the field of signal processing but also in
physics, econometrics, statistics, and computer science. In many problems encountered in signal
processing, it is possible to accurately describe the underlying statistical model using probability
distributions. Statistical inference can then theoretically be performed based on the relevant
likelihood function or posterior distribution in a Bayesian framework. However, most problems
encountered in applied research require non-Gaussian and/or nonlinear models to correctly account
for the observed data. In these cases, it is typically impossible to obtain the required statistical
estimates of interest [e.g., maximum likelihood (ML) or conditional expectation] in closed form
as it requires integration and/or maximization of complex multidimensional functions. This is
where Monte Carlo methods are valuable.
PIPE WALL ASSESSMENT FOR OTHER PIPE MATERIALS
All condition assessment methods rely on an excitation signal and measurement of a
response of the pipe system to the applied response. Most pipe wall assessment (PWA) methods
have focused on electrical or magnetic properties for excitation and response. For PCCP, remote
field eddy current transformer coupling has been successfully applied as a wire and pipe wall
condition assessment methodology using different insertion platforms (Mergelas and Atherton
1998, Mergelas and Kong 2001, Romer et al. 2008). For steel and iron pipe, magnetic flux leakage
has been applied to measure wall loss and damage (Hannaford et al. 2010). An overview of
methods is given by Prinsloo, Wrigglesworth and Webb (2011).
Acoustic methods for PWA have not received as much attention. Due to its composite
nature, PCCP has an intrinsic energy source for condition assessment excitation, the prestressing
wires. In the case of PCCP and this work, we use the unfortunate or uncontrolled energy release
of a wire break as the excitation signal and measure and analyze the acoustic response of the
structural pipe wall system. Other pipe materials such as steel, ductile iron, PVC and HDPE do
not have similar “intrinsic” excitation sources. Since these monolithic pipe materials do not have
an excitation source, one must be provided along with a sensor for measuring the response. The
concept for this project is to use tethered exciters with response sensors to in-situ measure the
change in radial stiffness as a function of axial position. We were unable to find any similar work
in the literature.
SUMMARY OF THE LITERATURE
While the literature for signal processing analysis is extensive, the work on processing and
analyzing signals from PCCP wire breaks has been limited. Amplitude analysis and statistics are
promising because they have been used intuitively by analyst to identify wire breaks, but
codification of the processes has been limited. Advanced mathematical methods have application,
but how robust these techniques are for field application is yet to be seen. The proof will be in the
analysis of both experimental and real data and understanding which methods translate under
which conditions.
25
CHAPTER 3: EXPERIMENTAL SET-UPS, TESTING PROCEDURES,
AND DATA MINING REPOSITORY
PASSIVE CONDITION ASSESSMENT OF PCCP FACILITY
Design of the Facility of PCA-PCCP Facility
The test set-up concept was conceived and designed by the investigators. Figure 3.1 depicts
the engineering drawings of the test set-up.
The design took into account several significant variables that were anticipated. These
included:




Frozen ground – Frozen ground was anticipated to be near the pipe, but not in contact
with the pipe as the water will always transfer heat to the surrounding. This was predicted
to be the exact case when the first cuts were planned to be performed in the experiment.
As the ground thaws continually further from the pipe wall, replicate cuts were planned to
be used to ascertain the effect of the increasing distance to frost line. The hope was that the
regression of frost would be slow enough to be tracked while the cuts were made.
Pipe size – Pipe size was not a variable that could be tested with the designed configuration.
However, much data from different pipe sizes and configurations is already available and
can be analyzed in the future for sensitivity to pipe size.
Water pressure – Water pressure was expected to have an effect on the spectral content,
but not to the degree expected as the compression level changes. At least two different
pressures were planned to be used during the tests to establish the sensitivity of the response
to water pressure.
Water flow – Water flow was not expected to contribute significantly to local spectral
content. The designed test setup could not provide flow. However, data from real world
monitoring of pipes can be mined to establish if there is an obvious sensitivity of spectral
content to flow rates.
27
Figure 3.1: Engineering drawing of test set-up
28
The City of Calgary’s pipe yard served as the test site for the pipe burial. In-kind support
from the City included the allocation and clean-up of a parcel of land for pipe burial. Figure 3.2
shows a site map of the burial setup, located at 14444 Bearspaw Dam Rd. NW, Calgary, AB.
Figure 3.2: Pipe burial site map
The burial of the pipe test set-up had to be performed before the start of winter. Any
additional delay would have pushed testing and data collection work to late-spring, which could
have resulted in a loss of the City of Calgary’s budgeted in-kind support.
Three (3) 42-inch PCCP-LCP sticks were donated (in-kind) by the City of Calgary for use
in this research project. Two (2) custom steel end plates were created to cap off either end of the
test setup; one end plate includes a 2-inch tap at the bottom for water (i.e. filling and draining);
one end plate includes a 1-inch tap at the top for bleeding air. See Figure 3.3. Three (3) 4-inch
ports were added with Victaulic grooves to attach sealing assemblies for the acoustic sensors. The
end plates are made of 1-inch steel and have cross bars welded on the surface to improve stability.
The pipeline joints were welded, a fairly common practice in some pipelines. Welded joints
were used in order to produce better acoustic transfer from pipe to pipe than would otherwise be
the case.
The design was modeled using computer software and confirmed to withstand loads in
excess of 90 psi.
29
Figure 3.3(a): End plate with 2-inch tap
(b): End plate with 1-inch tap
In accordance with industry practices, 10mm (0.4 inch) pea gravel was acquired for pipe
bedding. Enough gravel was acquired to place 4 inches below the pipe and 12 inches to the sides
and top of the pipe following industry standards.
With the goal of burying the PCCP sticks before the looming winter and to ensure the QA/QC
measures were met, the following methodology was followed.
1. Each pipe stick was visually inspected and showed no signs of deterioration.
2. One end cap was welded to the bell end of one pipe; the second end cap was welded to the
spigot end of another pipe.
3. A trench was excavated large enough to lay all three pipe sticks inside, approximately 69
feet in length, 11.5 feet in width, and 8 feet in depth. Worker access was required around
the joints of the setup and the trench had to be deep enough to lay the pipes 4 feet below
ground level (i.e. frost level).
4. The bottom of the trench was graded with a 4-degree slope so that air can rise to the high
end and be purged from the pipes when filling with water; likewise, water can be drained
out of the low end of the setup; a laser pointer was used to ensure the slope.
5. Half of the trench was bedded with the pea gravel and the other half was bedded using
native soil (both industry practices); two bedding practices are incorporated for
comparison.
6. Starting with the low end of the slope (i.e. water fill/drain end), a crane was used to lift
each stick of PCCP and lay it into the trench. See Figure 3.4.
30
Figure 3.4: Crane-lifted PCCP
7. A tether (cable) line was passed through the pipes and through access ports in order to pull
the fiber optic sensing cable at a later date; if the tether wire were to break prior to sensor
installation, then a rigid “fishing” cable will be inserted through the pipe to pull the fiber
optic cable.
8. The two pipe joints connecting the three PCCP sticks were externally welded for a stronger
and water-tight seal.
9. All ports/taps on the end plates were capped in order to prevent infiltration into the pipes.
10. In order to protect the pipe setup from the oncoming winter and ensure safety at the site,
the trench was fully backfilled using the excavated native soil.
11. Markers (i.e. stakes) were placed along the edges of the setup to mark the exact location of
each stick of pipe. See Figure 3.5.
Figure 3.5: PCCP stake positions
Work resumed on the buried test setup once the Calgary winter had passed and weather
conditions improved. The test setup was completely buried as a precautionary measure so two (2)
large pits were excavated on either end of the setup to provide access to the pipes. In addition to
the end pits, three (3) long pits were created on the top of the test pipe setup and a strip of outer
mortar coating was removed to expose the prestressing wires. See Figure 3.6.
31
Pit
Pipe end
access
Prestressing
wire
Figure 3.6(a): Exposed pipe ends/pits
(b): Pit window with exposed prestressing wire
Instrumentation Description
Acoustics
A fiber sensor was passed through the setup and a hydrophone was inserted for comparison.
The fiber sensor required careful fiber “splicing” onsite to route the optical pathways. Each splice
was examined to ensure that the total light loss through each fiber did not exceed the limits.
Various impact test strikes were performed to check the event locating accuracy of the
AFO system. One wire was cut using bolt cutters, recorded and analyzed to ensure that the system
was operational. See Figure 3.7.
Figure 3.7(a): Fiber insertion at pipe end (b): Fiber splicing (c): Impact testing for AFO
system
Since the signals are affected by the pressure within the pipe, calibration was performed
by generating wire breaks at different pressures and comparing the resulting data.
32
Because the sensor is a distributed sensor, the initial portions of the signal were unaffected
by the fact that the joints were welded. Later portions did start to show dispersion and attenuation
at a slightly slower rate than would otherwise be the case.
Since the ends of the pipes were not restrained by the welding, the wire breaks near the end
caps were used as reference to make a comparison with breaks near welded joints.
Volumetric Measurement
In parallel with the wire cut tests, a volume test was performed to examine the loss of
compression as prestressing energy was released. Specifically, a relation between the number of
sequential wire cuts and the amount of additional water to maintain pressure was recorded.
A specialized device was designed and built to maintain a constant pressure as wire cuts
proceeded, see Figure 3.8.
Figure 3.8(a): Constant pressure device at pipe end (b): Water reservoir
The volume of water added to the pipe to maintain 62 psi versus the number of sequential
wire cuts was recorded and mapped out. See Figure 3.9. A total of 4 liters (1.05 U.S. Gal) was
required to maintain the pressure at 62 psi at the conclusion of all wire cuts.
33
Figure 3.9: Volume of water added vs. number of sequential wire cuts
Test Procedures
Cut Sequence Rationale
Specially modified bolt cutters were used to manually cut the prestressing wires, as
depicted in Figure 3.10.
Other methods were initially examined; such as acid environment, electrically driven
corrosion cells, and hypothermic shock. The amount of acid required to break hundreds of wires
presented a serious hazard and so the method was discarded. The time required to corrode so many
wires electrochemically also caused that method to be discarded. In the end, the bolt cutters
provided satisfactory results.
For typical in-service pipe, the propensity of the position of wire breaks to progress away
from the source point—often a joint—assisted the investigators in making the decision to cut wires
in the same manner progressing away from a joint. Earlier work (Bell and Paulson 2010) did
indicate that as each wire breaks, the adjacent wires are exposed to a sudden increase in strain; an
observation that supports the progression of breaks.
Wires were cut in a systematic fashion (Figure 3.11) to control the displacement of
prestressing energy over each individual PCCP spool. The loss of prestress energy was evident
from the audible and visual separation of the prestressing wires after each cut.
34
Figure 3.10: Bolt cutters used to
simulate wire breaks
Figure 3.11: Wires cut in a systematic
fashion
Data Collection and Storage
The fibers were routed to an onsite workstation
(Figure 3.12) that housed the AFO data acquisition unit
(DAQ). The DAQ controlled the whole system—it detected
and recorded the acoustic events. The investigator’s
engineers were onsite to oversee the setup and test the
system to ensure that performance met or exceeded
standards. Each time a wire was cut, the DAQ would detect
and record the event.
Wire break simulation experiments were performed
on the 42-inch LCP in two phases separated by about Figure 3.12: Onsite workstation
Figure 3.12: Onsite workstation
90 days. The objective was to collect data with a variety of
levels of strain relief with each increasing wire break. After
completion of the buried PCCP test facility (Figure 3.13), cuts started in July 2012 at Joint D1-D2
and moved South on Pipe D2, sequentially cutting 25 wires. A few days later cutting continued
with another 25 wires, resuming the sequence at wire 26 to wire 50. In October, the investigator’s
engineers counted out to wire 100 on Pipe D2 from Joint D1-D2. Starting with wire 100, wires
were cut in sequence south to north to wire 51, meaning that wires were cut progressively moving
toward the first group of 50 wires already cut.
35
Figure 3.13: Orientation of the buried PCCP test facility
PIPE WALL ASSESSMENT DIP FACILITY
Design of the Facility
Five (5) spools of 12-inch ductile iron (DI) pipe were assembled to test the acoustic
response of non-PCCP pipe. The earth displaced during the PCCP burial was used to cover the DI
test setup and simulate the compaction of a buried pipeline. See Figure 3.14.
Figure 3.14(a): 12-inch ductile iron pipe
(b): Buried with earth displaced from PCCP test
The spools were connected using four (4) different flange/clamp methods. Each method
represented a difference in stiffness that can be further examined. Up to six (6) additional clamps
were placed in succession to simulate an increase in hoop stiffness along one spool of DI pipe. See
Figure 3.15.
36
Figure 3.15(a): Various joint connections
(b): Addition of up to six (6) additional clamps
Description of Instrumentation
Ideally a fiber optic cable would be used to capture acoustic events. However, the spatial
resolution of a fiber optic cable is constrained by the ability to measure the time of arrival of
perturbations of light. A 1M resolution requires that the light be sampled at 100,000,000 (10E8)
samples per second. To resolve a 1% change over a 1m length, the sampling would need to be at
10E10 samples per second. Currently, such a resolution is difficult to achieve.
So instead, custom pulsers were installed on the pipe end caps to generate acoustic pulses
and excite the pipe. An acoustic sensor was used to capture the response.
Test Procedures
The acoustic sensor (hydrophone) was passed through the pipe and recorded the pulses at
different points to create an acoustic profile of the pipe. The pulser travelled concurrently a fixed
distance from the hydrophone. See Figure 3.16.
Figure 3.16: Example of test procedure
37
The pipe was then excavated at one location between joints, a clamp was added to the pipe,
and the test procedure was performed again. Another clamp was then added and the test procedure
performed again. This continued until a total of six (6) clamps were added.
EXISTING IN-SITU MONITORING SYSTEMS
In addition to the field-based destructive testing of buried PCCP conducted during the
course of this project to collect controlled acoustic data associated with wire breaks, delaminations
and other events preceding PCCP failure, existing in-situ systems were examined.
PCCP owners around the world have invested billions into their PCCP assets and millions
into condition assessment and monitoring of these assets.
Overview of Monitoring Systems
With over 5,000 wire breaks recorded to date, Pure Technologies possesses the only known
existing database of spontaneous wire breaks in operating pipelines as detected with distributed
fiber optic sensors. Some of the pipes monitored with fiber optic sensors over past years have been
excavated and inspected, affording information as to the condition of pipes in which spontaneous
wire breaks were detected. Importantly, this allowed the investigators to compare the results in the
field with the output of models and algorithms developed from the buried, controlled test described
above. The feedback loop created in this way improved the practical applicability of results from
this work.
Several participating utilities had expressed intent to perform additional validation(s) of
AFO-monitored distressed pipe sections during the course of this project. These validations are
planned to compare the buried, controlled test to field results for participating utility’s pipes. For
these external investigation Pure Technologies will combine its proprietary external nondestructive, electromagnetic inspection tool with visual and sounding inspection to minimize
disruption to the PCCP owner.
The investigators have analyzed the empirical data from this project and have applied
different methodologies to the existing data.
Participant Support
The investigators secured the participation of PCCP owners from across the United States
and Canada. The monitored length of just the participant pipelines exceeds 120 miles over 19
separate installations.
The investigators applied the mathematical methods and acoustic signal profiles
characteristic of PCCP failure to the utility participant AFO data. All of the participant’s AFO data
was investigated for mining. The investigators used prior validations to verify the mathematical
models when feasible. The investigators had identified two well-controlled prior validations of
AFO-monitored pipelines and secured the PCCP owners participation and contribution of the
validation data to this project. Only these prior validations were counted as in-kind contributions
to this project.
PCCP owners without active AFO-monitored pipelines were also included as in-kind
participants. PCCP owners have a long history of applying many different condition assessment
and monitoring technologies to their pipelines with varying levels of success. The investigators
have sought to create the most practically applicable AFO-based condition assessment technique
38
by involving PCCP owners throughout the project. Workshops were held with water utilities with
PCCP in their systems (tens to hundreds and possibly thousands of miles) throughout the project
duration. Workshops were held concurrently with major conferences favored by PCCP owners and
operators, and the utility participants:



AWWA Annual Conference and Exhibition (ACE)
ASCE Pipelines
AWWA Distribution System Symposium (DSS)
The PCCP User Group is a utility-based group of approximately 50 members that meet
periodically to discuss issues relevant to PCCP. The members have typically had pipeline failures
or other issues and are the best source of information not available in open literature. The PCCP
Users Group typically meets in conjunction with ASCE Pipelines each year. One of the project
workshops was held in conjunction with the 2012 PCCP Users Group at ASCE Pipelines in Miami,
FL.
Participant Monitoring Systems
The utilities that had committed to participating in this study all had a vested interest in
furthering PCCP condition assessment through AFO monitoring to extend the service life of their
PCCP infrastructure and more importantly to reduce the risk of catastrophic failures. Some utilities
simply had PCCP in their systems and sought to provide guidance and direction for the scope and
deliverables of this project. Other utilities had already been actively monitoring their PCCP using
AFO for the purpose of identifying wire breaks and the associated lack of structural integrity. A
third group not only had active AFO monitoring systems, but had committed to contributing prior
and/or future AFO validation studies to this project. The participating utilities provided a wide
variety of types and sizes of PCCP, environmental conditions in which PCCP had been buried, and
pipe manufacturers.
While the Foundation is focused on advancing the science of drinking water by providing
utilities and drinking water suppliers with practical solutions to complex issues, PCCP has also
been used widely in non-drinking water applications. Arizona Public Service Company (APS)
operates 37 miles of PCCP transporting treated sewage effluent from a major sub-regional
wastewater treatment plant to the Palo Verde Nuclear Generating Station. APS was committed to
participating in this project.
Howard County owns and operates approximately 20 miles of PCCP, of which
approximately 7 miles is AFO monitored. Howard County regularly performs validations of wire
breaks detected by the AFO system. In early February 2011 a total of seven pipes, including both
single-wrapped and double-wrapped PCCP, were excavated and removed from the 36-inch
Southwestern Transmission Main. This pipeline has been AFO-monitored since 2007. Pure
Technologies has reported wire breaks from AFO monitoring on these pipes and the purpose of
the validation is to confirm the locations and quantities of wire breaks reported. Certain acoustic
data analysis methods or algorithms developed during this project can also be applied to the
Southwestern Transmission Main AFO data and the field validation results used to test the
performance of the algorithms. Figure 3.17 shows the excavation of this 36-inch pipeline on
February 2, 2011.
39
Field investigations were also
performed on WSSC’s 96-inch
Potomac Transmission Main. A rapid
succession of wire break activity was
reported to WSSC by Pure
Technologies
based
on
AFO
monitoring. It is believed that this pipe
was within hours or days of
catastrophic failure when it was shut
down and replaced. Figure 3.18 shows
wire damage on the pipe exterior with
a hollow and longitudinal crack in the
corresponding interior location. AFO
monitoring alerted WSSC to this
highly distressed pipe prior to failure.
Figure 3.17: Excavation of Howard County's 36-inch The condition of the pipe, mortar, and
wires was carefully studied and
southwestern transmission main
documented before removal. The
acoustic data analysis methods and
algorithms developed for this research was applied to this pipeline and the prior field studies used
to validate the research. The value of a portion of the field studies was contributed to this project
by WSSC.
Figure 3.18: Damage to WSSC's 96-inch Potomac transmission main
The following is a list of all the participating utilities.







Central Arizona Project
City of Calgary
City of London
Dallas Water Utilities
Howard County Department of Public Works
Louisville Water Company
40






Metropolitan Water District of Southern California
Providence Water
Tarrant Regional Water District
Tucson Water
41
CHAPTER 4: RESULTS AND SIGNAL ANALYSIS FOR PCA PCCP
DATA
PRESENTATION OF ACOUSTIC TEST DATA
Each successive wire break from the experimental test site in Calgary was recorded
acoustically. An example of a raw signal from the fiber sensor is presented in Figure 4.1.
Figure 4.1: Fiber sensor raw signal from experimental test site wire cut
The wire break sensor data from the experiment appeared to adequately mimic fieldapplied FOC acoustic monitoring system data.
The spectra of the fiber sensor and the hydrophone, installed for comparison, are shown in
Figure 4.2. The similarity between the two was noted.
43
Figure 4.2: Fiber sensor and hydrophone data
Observations and Analysis of PCCP Test Data
The energy of the cuts was examined near the start of the cut, approximately the first
10 msec of the cut. Specifically, the ratio of energy in the high frequency range was examined
(rather arbitrarily chosen as > 10 kHz) and the low frequency range (<10 kHz) changed with
increasing number of wire cuts. A decent negative correlation was found for the high frequency
energy of wire cuts performed in the pipe yard with wire cut number (r = -0.59), Figure 4.3.
44
Wire Cut Number
Figure 4.3: Pipe yard wire cut data
However, the same trend was not observed with the wire cuts produced in the Middlesex
County Utilities Authority (MCUA) study, Figure 4.4. Investigation later showed that these cuts
were done using a cutting torch.
Figure 4.4: MCUA wire cut data
In both the pipe yard wire cuts and the MCUA wire cuts, a slight positive correlation was
observed in the High Frequency Energy/Low Frequency Energy ratio (r = 0.318 and r = 0.347,
respectively). See Figure 4.5 and Figure 4.6.
45
Figure 4.5: Pipe yard wire cut frequency ratio vs. wire cut
Figure 4.6: MCUA wire cut frequency ratio vs. wire cut
The measured acoustic output from each of the 100 wire cuts from the buried test setup is
shown in Figure 4.7.
46
Figure 4.7: Measured acoustic output as a function of wire cuts
The graph of root mean square (RMS) power, as shown in Figure 4.8, is the plot of the
RMS power versus the second group of 50 wire cuts (i.e. wires 100 to 51). The extraction of
values was done using max amplitude and max RMS power with a 5 msec window, using Adobe
Audition. The observation was that the power changes significantly as the relaxation or recovery
length of the wire was reduced.
Figure 4.8: RMS average acoustic power vs. remaining wires
The implication is that not only frequency/spectral characteristics can be used as a predictor
in the change of pipe condition and approach to loss of structural integrity; acoustic power
characteristics require further investigation as a predictor.
ANALYSIS METHODS
The general process was to extract certain features using mathematical analytical methods,
apply each method to a group of data where a trend might exist, and examine the results.
47
Short-Time Fourier Transform
Ultimately, the Short-Time Fourier Transform (STFT) was selected as the most appropriate
analysis method. The experimental acoustic time domain data was transformed to frequency
domain for further analysis.
Wavelet Analysis
Analysis via wavelet transform was investigated as a means to glean additional useful
information from the signal data. However, for the purposes of this study, in which the signal of a
number of wire breaks are compared to each other, a simple wavelet transform was not expected
to provide any additional information which was not given by other analyses. If, for example, the
frequency content of the wire breaks changed as the damage on a particular pipe increased, the
expectation was that this effect would manifest itself in the HEMP analysis as a correlation
between the Half Energy frequency and the number of wire breaks.
Since the frequency spectra of a number of wire breaks is being compared with wire cut
signals and analyzed for trends with increasing number of breaks on a pipe, it was considered
likely that any consistent changes in the frequency content of the wire cut signals would manifest
themselves in our analysis of the frequency of half energy for each wire break.
For example, if a scenario is imagined in which the frequency spectrum of each successive
wire cut is different from the wire cuts previous, this would manifest as a correlation between the
HEMP frequency and the wire cut number. Indeed, a wavelet analysis would also reveal this
information, but a direct comparison of the frequency spectra obtained using a standard Fourier
transform is simpler and should be equally effective in this case.
A wavelet analysis attempting to find changes in the temporal position of certain
frequencies within all the wire cuts, and comparing this information between cuts may be a next
step if further analyses are to be done. However, the investigators have for several years been using
the short-time Fourier transform to analyze data from wire breaks and other pipeline noise. The
short-time Fourier transform is similar to a wavelet transform, in that it provides some temporal
information as to the frequency content of the signal. But in the investigator’s experience using
the short-time Fourier transform has not provided any indication of any obvious changes in the
temporal distribution of frequencies in wire break signals as a pipe accrues more damage that
would not be revealed by a HEMP analysis.
Monte Carlo Analysis
At this time, the value of Monte Carlo techniques to the research at hand are not clear. At
this point, FFT, WFT and wavelet methods seem most appropriate.
APPLICATION OF BEST CANDIDATE ANALYSIS METHOD TO REAL WORLD
DATA
Once the data was transformed to frequency domain, analysis continued.
Selected Data
From the participant supporters and database of existing in-situ monitoring system wire
break data, WSSC, Ottawa, San Diego, Tucson, and Cutzamala were selected as having pipe
48
systems with wire break data most comparable to the experimental Calgary yard setup. These
monitoring sites had pipes verified to have been in imminent failure when excavated.
It should be noted that most of the select data sites are embedded cylinder pipe, whereas
the Calgary test site is lined cylinder. In addition, differences in the field loading conditions, in
particular, ratio of design pressure to operating pressure may impact the effectiveness of the
analysis and comparison.
Characteristics and Comparison of Results
RMS Power Analysis
First, an analysis of the average RMS power (amplitude analysis) was performed using
data from the Calgary pipe yard, as well as with the selected database data.
The average RMS power data appears to reveal some trends, however this is only clearly
visible in the experimental pipe yard data. Initially the average RMS power had a slight increase
with additional wire cuts. However, after a certain number of wires were cut, the average RMS
power decreased substantially as each successive wire was cut. This trend was observed with pipe
D2 as shown in Figure 4.9.
RMS average acoustic power released vs. number of remaining wires
‐50
‐52
‐54
‐58
‐60
‐62
RMS Power in dB
‐56
‐64
‐66
‐68
‐70
50
45
40
35
30
25
20
15
10
5
0
Number of Remaining Wires
Figure 4.9: Average RMS power from pipe yard D2
This plot only shows the second set of cuts (51–100) because these were done on the same
day and in the same direction. The first set of cuts on pipe D2 (1–50) were performed on a different
day and started at the other end of the pipe, so combining them on the same plot would not yield
the best comparison of signals.
49
Also compiled was a plot showing all the cuts at once, see Figure 4.10. As observed, the
first set of cuts (1–50) was much noisier than the second set (50–100). This may have had
something to do with background noise or adjusting of the hardware. In any case, cuts 50–100
were much cleaner and yielded a more discernible trend.
Figure 4.10: Average RMS power from pipe yard D2 with all cuts
A similar trend was observed in pipe D3 as shown in Figure 4.11. Only 50 cuts were
performed on pipe D3, so that is why that plot only goes from 1–50.
50
D3 Average RMS Power
‐77
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
‐77.1
‐77.2
RMS Power (dB)
‐77.3
‐77.4
‐77.5
‐77.6
‐77.7
‐77.8
‐77.9
‐78
Wire Cut Number
Figure 4.11: Average RMS power from pipe yard D3
Figure 4.12 depicts the first 20 cuts performed on pipes D2 starting from the D1 Joint, the
first 20 cuts starting from the D3 joint, and the first 20 cuts on D3 on the same plot for comparison.
Figure 4.12: RMS power from pipe yard D2 and D3
51
The energy of the cuts on pipe D3 were very consistent, but were more varied on pipe D2.
Some of the first few cuts done on D2 were not reliable due to hardware adjustment, so the data
appears skewed for the “D2 from D1 joint” data. The first 20 cuts on D2 from the D3 joint were
better and showed the similar upward and then downward trend.
When the analysis was also performed on data from the monitoring site database, a similar
trend was observed. However, as the sample size was much smaller for the in-situ database data,
it is difficult to confirm the correlation with certainty. Data from WSSC, Ottawa, San Diego, and
Tucson is illustrated in Figure 4.13, Figure 4.14, Figure 4.15, and Figure 4.16, respectively.
Average RMS Power vs. Wire Break Number ‐ WSSC
0
0
1
2
3
4
5
6
7
8
‐10
‐20
Energy (dB)
‐30
y = ‐0.5985x2 + 8.5645x ‐ 75.1
R² = 0.9884
‐40
‐50
‐60
‐70
‐80
Wire Break Number
Figure 4.13: Average RMS power from WSSC AFO site
52
9
10
Average RMS Power vs. Wire Break Number ‐ Ottawa
‐58
0
2
4
6
8
10
12
14
‐58.5
Energy (dB)
y = 0.047x ‐ 59.722
R² = 0.065
‐59
‐59.5
‐60
‐60.5
Wire Break Number
Figure 4.14: Average RMS power from Ottawa AFO site
Average RMS Power vs. Wire Break Number ‐ San Diego
‐36.5
0
2
4
6
8
10
‐37
‐37.5
Energy (dB)
‐38
‐38.5
‐39
y = ‐0.1404x ‐ 37.885
R² = 0.2149
‐39.5
‐40
‐40.5
‐41
Wire Break Number
Figure 4.15: Average RMS power from San Diego AFO site
53
12
14
Average RMS Energy vs. Wire Break Number Tucson 1
Wire Break Number
‐10
5
7
9
11
13
15
17
19
21
‐12
‐14
Energy (dB)
‐16
‐18
‐20
‐22
‐24
‐26
‐28
y = ‐0.2435x2 + 6.712x ‐ 65.753
R² = 0.5567
‐30
Figure 4.16: Average RMS power from Tucson AFO site
The AFO site in Cutzamala, Mexico has several pipes with a large number of breaks (i.e.
more than 50). This is one the largest number of AFO breaks on single pipes contained within the
database. The RMS power plot was performed on one such pipe that was excavated and confirmed
to be damaged. See Figure 4.17.
Figure 4.17: Average RMS power from Cutzamala AFO site
54
Here, there appears to be no correlation with wire break number and RMS power. It was
hypothesized that the breaks on this particular pipe were due to hydrogen embrittlement (HE).
Since HE breaks do not reduce the integrity of the pipes as much as other types of wire breaks, it
extends that no change in RMS power may be seen. This phenomenon, if confirmed, may prove
to be beneficial as a random distribution of RMS power could potentially help identify HE vs.
corrosion-related wire breaks at AFO sites.
In general, RMS energy appears to have greater sensitivity to wire cuts and field-collected
data although specific and detailed differences in manufacturing of PCCP may influence
effectiveness of post processing.
The correlation of average RMS power to wire breaks appears promising to be able to
extend additional signal processing capabilities to an existing dataset. Utilities that have been
monitoring for sound should have a high enough degree of signal fidelity to facilitate analysis,
assuming the bandwidth of current practice in fiber optic is sufficient. Noting that the currently
available bandwidth is typically from a few Hz to more than 30 kHz, there is an expectation that
current practice in acquisition need not be altered. To verify this, the duration of acoustic event
captured surrounding each wire break was lengthened to 10 seconds to potentially allow very low
frequency resonances to be identified. However, no additional useful information was captured
from the longer time interval.
HEMP Analysis
Next, a peak frequency analysis was performed. This half energy (HEMP) analysis was
performed on the experimental pipe yard data and selected AFO sites.
The HEMP analysis from Day 2 in the pipe yard showed a slight positive correlation
between the change in median frequency and wire cut number, and Day 3 in the pipe yard showed
a moderate negative correlation between the change in median frequency and wire cut number.
See Figure 4.18 and Figure 4.19.
55
HEMP Frequency vs. Wire Cut Number ‐ D2
30000
HEMP Frequency (Hz)
25000
20000
15000
10000
y = 267.94x ‐ 1693.2
R² = 0.1204
5000
0
50
55
60
65
70
75
Wire Cut Number
Figure 4.18: HEMP analysis from pipe yard D2
HEMP Frequency vs. Wire Cut Number ‐ D3
25000
HEMP Frequency (Hz)
20000
15000
10000
y = ‐180.88x + 16737
R² = 0.2672
5000
0
0
5
10
15
20
25
30
35
40
45
Wire Cut Number
Figure 4.19: HEMP analysis from pipe yard D3
Neither trend was especially observed in the selected data from other pipe sites, although
this could be in part because the sample size of wire breaks is so much smaller for the AFO sites.
See Figure 4.20, Figure 4.21, Figure 4.22, and Figure 4.23 for HEMP analysis for sites at WSSC,
Ottawa, San Diego, and Tucson, respectively.
56
HEMP Frequency vs. Wire Break Number ‐ WSSC
18000
16000
HEMP Frequency (Hz)
14000
12000
10000
8000
y = ‐174.74x + 13066
R² = 0.032
6000
4000
2000
0
0
1
2
3
4
5
6
7
8
Wire Break Number Figure 4.20: HEMP analysis from WSSC AFO site
HEMP Frequency vs. Wire Break Number ‐ Ottawa
18000
16000
HEMP Frequency (Hz)
14000
12000
10000
y = ‐102.25x + 15461
R² = 0.0938
8000
6000
4000
2000
0
0
2
4
6
8
10
Wire Break Number
Figure 4.21: HEMP analysis from Ottawa AFO site
57
12
14
HEMP Frequency vs. Wire Break Number ‐ San Diego
14000
12000
HEMP Frequency (Hz)
10000
8000
6000
4000
y = 38.027x + 8662.5
R² = 0.0158
2000
0
0
2
4
6
8
10
12
14
Wire Break Number
Figure 4.22: HEMP analysis from San Diego AFO site
Tucson 1 HEMP Frequency vs. Wire Break Number
30000
HEMP Frequency (Hz)
25000
20000
y = 95.103x + 11767
R² = 0.0098
15000
10000
5000
0
0
2
4
6
8
10
12
14
16
18
Number of Wire Breaks
Figure 4.23: HEMP analysis from Tucson AFO site
HEMP analysis from FFT data initially seemed to be useful based on data from the
SDCWA failures. However, when it was applied to a more controlled and broader spectrum of
results, it does not appear useful due to lack of any sort of consistent correlation.
58
CHAPTER 5: PIPE WALL ASSESSMENT (NON-PCCP) RESULTS
PRESENTATION AND ANALYSIS OF PWA TEST DATA
In the DIP experimental setup, the pulse generated by the pulser was received by the roving
hydrophone. The time interval between the generation of the pulse and the detection of the pulse
at 10 feet was plotted versus the pulser position. See Figure 5.1. Each pipe joint clearly appeared
in the data as a change in pipe hoop stiffness. The largest variations appeared at restrained flex
couplings, with smaller effects at the flexible sleeve coupling and flanged joints.
Figure 5.1: Pipe wall assessment data with various pipe joint connections
A series of clamps was then added, with the test procedure performed and the data recorded
between each successive clamp addition. The flight time for no clamps, two (2) clamps, four (4)
clamps, and six (6) clamps was plotted verses pipe position. See Figure 5.2. Again, the data clearly
showed the increase in hoop stiffness as each clamp was added.
59
Figure 5.2: Pipe wall assessment data, illustrating effect of additional clamps
The ability to successfully distinguish between areas of varying stiffness may be useful in
identifying areas of deterioration in non-PCCP. Failure mechanisms such as uniform metal loss
or longitudinal cracking would appear as sections of diminished stiffness. These failure
mechanisms are highly localized and require a technique with sufficient resolution to distinguish
varying levels of hoop stiffness in less than a pipe length. Highly localized variations in stiffness
(e.g. reduction is wall thickness) were not investigated as would be the case for most corrosion or
pitting in iron piping systems, but this technique appears promising for the pipe wall assessment
of non-PCCP.
60
CHAPTER 6: SUMMARY AND CONCLUSIONS OF THE RESEARCH
The owners or users of PCCP currently live with the nagging concern or fear that
continuing degradation of their buried pipe will cause loss of continuity of service, property
damage or, in the worst case, loss of life. The newspapers and television news are periodically
smattered with the devastation that can be wrought by such a failure. The risk of a failure is the
product of the likelihood or probability of failure and the consequence or cost of that failure. Little
can be done to manage the consequence of a failure since water delivery is essential and right of
ways are increasingly more urbanized as a result of the available water supply. The only thing that
can be done is to manage the likelihood of failure by improving the transformation of direct
structural condition (acoustic) data to actionable information. Based on the earlier work by the
project Principal Co-Investigators, it appeared that existing technology had sufficient resolution to
collect data in the time domain with sufficient resolution to detect trends in pipe condition (Travers
1994, Bell et al. 2009). It did not appear that advancement in technology was necessary, just
improvement in analysis, collection of pedigreed and confirmed baseline data for use in the
development of the analytical methods, and finally field verification of the methodology.
A test setup utilizing three lengths of LCP PCCP was installed in Calgary to experiment
with wire breaks and the resultant acoustic signals. The raw signal was processed using Fourier
transforms and the ensuring spectra data was analyzed for average RMS power and peak frequency
(HEMP) analysis. While the HEMP analysis did not prove useful, the average RMS power showed
promise for correlating the signal amplitude with the number of broken wires. A general trend
could be seen in RMS amplitude as prestressing wires wraps were successively cut. Data from the
real-world monitored pipes were examined to see if the same pattern was evident which showed
some evidence of such; however, the samples sizes were not large enough to confirm or deny the
overall trend.
The practice of managing PCCP risk may now be simplified. We are “fortunate” with
PCCP that 1) the prestressed high strength wires are the primary structural members of the pipe
design (if you monitor and manage wire breaks you can help monitor and manage pipe integrity
and likelihood of failure), and 2) when a wire break occurs, the energy release provides a method
of excitation by which the integrity or structural response can be measured. Fundamentally, this is
what all condition assessment methods do; excite, measure the response and then analysis of data
provides information for action. For PCCP owners, the deterioration can be monitored as the wires
break and excite the pipe structure and the structural condition assessed. Bell and Paulson (2010)
coined the phrase, “Noisy Pipes are not Happy Pipes.” In essence, this was the first step in using
acoustic data for condition assessment. It is similar to using your hand to determine if you have a
fever. The new method should effectively be a “thermometer” for the condition of the pipe.
In its optimal result, PCCP owners would be able to operate their pipelines right to the
brink of failure prior to taking them out of service for repair or rehabilitation. In this manner,
rehabilitation would be planned and capital expenditures optimized.
Further down the road, it may be possible to use the same fundamental processes to
evaluate other pipe materials. In order to do this, new technology for excitation and response may
be necessary since other pipe materials do not have the advantage of excitation by wire breaks.
Having said that, the first step is to develop the methods using PCCP and then move the process
along.
Since the correlation of average RMS power and wire breaks was found, it can be
immediately applied to all pipelines where AFO data exist, including retrospectively. If a pattern
61
in RMS amplitude such as that seen from one of the test pipes becomes evident in any monitored
pipes, this will be communicated with the pipeline owners to take appropriate investigative
measures. In the long term, it may be possible to conduct analysis in real time to allow pipe
condition to be understood as a continuum rather than single data point of condition or the result
of failures.
62
CHAPTER 7: FUTURE RESEARCH SUGGESTIONS








Perform further research with wavelet analysis to determine if any meaningful data can be
extracted beyond what can be determined from FFT or STFT.
Further investigate the value of Monte Carlo techniques to the research at hand.
Investigate if the time between wire breaks, or wire reliability trending, could prove useful
in predicting time to imminent failure.
Perform additional experimentation with different field loading conditions, in particular,
the ratio of design pressure to operating pressure, to determine the effect on acoustic signal
processing.
Investigate further the effect of hydrogen embrittlement on the distribution of average RMS
power vs. wire cuts/breaks. This phenomenon, if confirmed, may prove to be beneficial as
a random distribution of RMS power could potentially help identify HE vs. corrosionrelated wire breaks at AFO sites.
In PWA, investigate highly localized variations in stiffness to further qualify this method
in real-world situations (e.g. pitting or corrosion).
Examine a new technology for excitation and response of non-PCCP materials, since nonPCCP materials do not have the advantage of excitation by wire breaks.
Further examine RMS power patterns from currently installed AFO sites.
63
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69
ABBREVIATIONS
AE
Acoustic event
ACE
Annual Conference and Exhibition
AFO
Acoustic Fiber Optic
AM
Amplitude modulation
APS
ASCE
American Society of Civil Engineers
ASME
American Society of Mechanical Engineers
ASTM
American Society for Testing and Materials
AWWA
American Water Works Association
DAQ
Data acquisition unit
dB
Decibels
DFT
Discrete Fourier transform
DI
Ductile iron
DIP
Ductile iron pipe
DSS
Distribution System Symposium
DWT
Discrete wavelet transform
ECP
Embedded cylinder pipe
ed.
editor
FFT
Fast Fourier transform
FM
Frequency modulation
FOC
Fiber-optic cable
ft.
feet
Gal
Gallon
gm
gram
HDPE
High-density polyethylene
HE
Hydrogen embrittlement
71
HEMP
Peak frequency analysis
Hz
Hertz
IE
Impact echo
IEEE
Institute of Electrical Engineers
kHz
Kilohertz
lb
pounds
LCP
Lined cylinder pipe
m
Meters
M
Million
ML
Maximum likelihood
mm
Millimeters
MCUA
Middlesex County Utilities Authority
msec
milliseconds
NACE
NACE International, formerly National Association of Corrosion Engineers
PCA
Passive condition assessment
PCCP
Prestressed concrete cylinder pipe
PSD
Power spectral density
psi
Pounds per square inch
PVC
Polyvinyl chloride
PWA
Pipe wall assessment
QA/QC
Quality assurance/quality control
RMS
Root mean square
ROC
Receiver operating characteristic
SDCWA
72
STFT
Short-Time Fourier Transform
U.S.
United States
vs.
versus
WFT
Windowed Fourier transform
WSSC
73

4360 - Water Research Foundation

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