Durability Growth through HALT HASS Testing

HALT and Reliability Workshop
Elite Electronic Engineering
Steve Laya
630-495-9770 x 119, [email protected]
HALT and Reliability Workshop
Topics Covered

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Reliability and Planning
Overview of Reliability Concepts‐ Distributions and life estimation
Reliability metrics: MTBF, failure rate, R (Reliability) and C (Confidence)
Accelerated Testing‐ Uses and cautions; Models for temperature and humidity
Accelerated vibration models; Other accelerated tests
Vibration Techniques‐ Electro‐dynamic, Repetitive Shock, Servo‐hydraulic
Characteristics of vibration produced, relative damage potential, recommended use.
How, When, and Why to use HALT and Accelerated Testing
How Do You Define Reliability?

“…the ability of a system or component to perform its required functions under
stated conditions for a specified period of time”

The probability of success
The capability to perform as designed
Reliability, Availability, Maintainability (RAM) , Safety, Testability
Number of failures over a period of time MTBF, MTTF, Failure Rate, Hazard Rate
Mathematical definition




Where h(t) is the hazard function or hazard rate
How Do You Evaluate Reliability?
Statistics
Probability Theory
Reliability Theory
Hazard Analysis
FEMA
FTA
Reliability Handbook Prediction
Weibull Analysis
Accelerated Life Testing
Maximum Likelihood Estimates
Markov Analysis
Physics of Failure
Design Review
Sneak Circuit Analysis
Reliability Demonstration /Growth
HALT/HASS
Which tool
to use?
Managing Reliability‐
The Core Elements of a Reliability Program.
1 Understand
Customer
Requirements



Environment
Duty Cycle
Reliability Goals
2 Feedback from Similar Components




FRACA- Test Failures, Production Failures, Field
Failures
Third Party Assessments- J.D. Powers &
Associates
Warranty Returns- Return Rates, Feedback from
Customers and Technicians
Development Testing
3 Begin the FMEA

Update throughout the
process
4 Intelligent Design


Lets’ do some
testing!
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7 Change Control

Qualify all any changes in engineering,
production, or supply base.

6 Manufacturing

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Production parts validation
Qualify production process with Cpk = 1.67
Ensure compliance with SPC program
100% sampling 1st week of production reduce
as necessary
Develop control plans for each drawing
Evaluate measurement error for in process
measurements
Qualify storage, transportation, and installation
systems
Use Design Guides
Incorporate lessons learned from previous work
Parameter Design- Choose design variable levels
to minimize effects of uncontrolled variables
Tolerance Design- Scientifically determine correct
drawing specifications
Schedule Periodic Design Reviews
Design with Information from Development
Activities
Sneak Circuit Analysis, HALT, Step-Stress to
Failure, Worst-Case Tolerance Analysis
5 Concept Validation & Design Validation



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Early Design Phase Engineering Development Tests
Independent Verification Test (outside of
engineering)
Specify From List of Validated Subsystems &
Components
System Simulation
Ref: Accelerated Testing, Dodson & Schwab
Testing for Reliability
1.
Customer Specified Requirements

2.
Identify and Design-out Latent Defects

3.
Competitive or new products (Qualitative)
Estimation of Reliability Parameters


5.
HALT and other early short duration tests (Qualitative)
Comparison of Products

4.
Auto/Truck OEM, RTCA DO-160, MIL-810 (Qualitative)
(underlying distribution, point estimate, confidence
MTBF, MTTF (Quantitative)
Reliability & Confidence (Quantitative) (graphically or statistically)
Contractual Compliance to a Specific Metric


Reliability Demonstration (Quantitative)
Reliability Growth (Quantitative)
Estimation of Reliability Parameters

Specify the test

Define Test Objective


Lab Test or Field Test





Temperature and Current
Assign Test Durations. Apply Acceleration Factors


High impedance fault due to Electromigration
Specify Environments, DUT Configuration, Failure Criteria


Lab Test
Evaluate failure modes, failure mechanisms


Determine the MTTF and failure rate for…
Arrhenius Model, Power Law Mode
Run Test and Collect Data
Record times to failure- Plot a Histogram
Fit the histogram shape to a failure distribution
Estimate distribution characteristics of interest by “parametric approach”

Parametric means related to a distribution
Estimation of Reliability Parameters
Collect the data and plot a Histogram
•
•
•
•
Divide x-axis into intervals
Count the number of failures occurring in each interval
Scale the y-axis for the maximum number of counts
Fit a curve to the plot
• Ex. Start with 100 samples on a powered elevated
temperature life test. Count remaining units at each interval.
Interval Remaining
(Hour)
Units
1
90
2
81
3
73
4
66
5
59
6
53
7
48
8
43
9
39
10
35
11
31
12
28
13
25
14
23
15
21
16
19
17
17
18
15
19
14
20
12
21
11
22
10
23
9
24
8
25
7
26
6
27
6
28
5
29
5
30
4
31
4
Failures
10
9
8
7
7
6
5
5
4
4
3
3
3
3
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
0
0
Cumul
Failure
10
19
27
34
41
47
52
57
61
65
69
72
75
77
79
81
83
85
86
88
89
90
91
92
93
94
94
95
95
96
96
Estimation of Reliability Parameters
Plot the Data
•
Create a Relative Frequency Plot
•
Relative Frequency = Class Count
Total
Interval
1
2
10
17
Class Count
Percentage
per Total
10/100
0.10
9/100
0.09
4/100
0.04
2/100
0.02
Estimation of Reliability Parameters
Probability Density Function (PDF)
•
•
Relative likelihood for the variable to take on a given value.
The probability density function is nonnegative everywhere,
and its integral over the entire space is equal to one
•
•
•
•
•
Ex1:
N=100
 = 0.1
Evaluate at 10 hours
f(10)= 0.036
•
•
•
•
•
Ex2:
N=100
 = 0.1
Evaluate at 20 hours
f(20)= 0.0135
Probability Density Functions (PDFs) to
Cumulative Distribution Functions (CDFs)
•
Sum the area beneath the PDF
•
CDF provides a probability of failure relative to xaxis (time, cycles, life)
•
The compliment of the CDF is the Reliability
Function.
•
Reliability (x) = 1-CDF(x)
Where
 = failure rate
1/ = MTTF
Reliability
Expression for
Exponential
Distribution
Evaluate the Reliability Function
Examples
•
•
•
•
•
Ex1:
N=100
 = 0.1
Evaluate at 10 hours
R(10)= 0.36
•
•
•
•
•
Ex2:
N=100
 = 0.1
Evaluate at 20 hours
R(20)= 0. 135
Customer Provided Reliability Metrics
• = 1/MTTF
• Failure rate  = 0.1
• MTTF = 10
Reliability Point Estimates and Confidence
•
Calculate Confidence Intervals for Different Distributions
• Range of values bounded above and below within
which the true value is expected to fall.
• Measures the statistical precision of the estimate
• 90% confidence interval should contain the estimate
90% of the time
• Determine the interval within which the true
parametric values lies with a given probability for a
given sample size
• Determine the sample size required to assure with a
specified probability that the true parametric value
lies within a specific interval.
Exponential: Chi-Squared distribution
Normal: t-distribution
Weibull: See referenced resources…
Reliability Point Estimates and Confidence
Calculate Confidence Intervals
Exponential requires Chi-Squared Distribution
(Normal requires t-Distribution)
MTTF= 216hrs
115hrs
t* = time at which the life test is terminated
r = number of failures accumulated at time t*
T = total test time
Note: Ref: 2
a = acceptable risk of error
1 - a = confidence level
T = total test time
459hrs
Point Estimate for MTTF with Confidence Intervals
Example: Calculate MTTF with Confidence Intervals for Fixed
Truncation Time on 100 units, C=90%
MTTF =
Fixed Truncated Test
Lower One
Sided
Confidence
Bound
Two Sided
Confidence
Bound
n = number of items placed on test at time t = 0
t* = time at which the life test is terminated
r = number of failures accumulated at time t*
r* = preassigned number of failures
a = acceptable risk of error
1 - a = confidence level
T = total test time
866 =9.02 hrs
96
Total Test Time
Number of Failures

= 1-CL

= 1-0.9 = 0.1
/2 = 0.05
r = 96
2r+2 = 194
Lower One Sided Confidence Bound
2T
1732
c2(0.1, 194)
219.633
2T
2T
c2(0.05, 194)
(7.89, )
Two Sided
c2(0.95, 192) Confidence
Bound
1732
1732
227.496
160.944
(7.61, 10.76)
Interval Remain
Cumul Cumul
(Hour) Units Failures Failure Time
1
90
10
10
90
2
81
9
19
171
3
73
8
27
244
4
66
7
34
310
5
59
7
41
369
6
53
6
47
422
7
48
5
52
470
8
43
5
57
513
9
39
4
61
551
10
35
4
65
586
11
31
3
69
618
12
28
3
72
646
13
25
3
75
671
14
23
3
77
694
15
21
2
79
715
16
19
2
81
733
17
17
2
83
750
18
15
2
85
765
19
14
2
86
778
20
12
1
88
791
21
11
1
89
802
22
10
1
90
811
23
9
1
91
820
24
8
1
92
828
25
7
1
93
835
26
6
1
94
842
27
6
1
94
848
28
5
1
95
853
29
5
1
95
858
30
4
0
96
862
31
4
0
96
866
2T
1732
Chi Square Distribution Table
160.944 =CHIINV(0.95,20)
P
DF
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
0.995
143.545
144.413
145.282
146.15
147.02
147.889
148.759
149.629
150.499
151.37
152.241
153.112
153.984
154.856
155.728
156.601
0.975
153.721
154.621
155.521
156.421
157.321
158.221
159.122
160.023
160.925
161.826
162.728
163.63
164.532
165.435
166.338
167.241
0.2
206.182
207.225
208.268
209.311
210.354
211.397
212.439
213.482
214.524
215.567
216.609
217.651
218.693
219.735
220.777
221.818
0.1
215.371
216.437
217.502
218.568
219.633
220.698
221.763
222.828
223.892
224.957
226.021
227.085
228.149
229.213
230.276
231.34
0.05
223.16
224.245
225.329
226.413
227.496
228.58
229.663
230.746
231.829
232.912
233.994
235.077
236.159
237.24
238.322
239.403
0.025
230.064
231.165
232.265
233.365
234.465
235.564
236.664
237.763
238.861
239.96
241.058
242.156
243.254
244.351
245.448
246.545
0.02
232.146
233.251
234.356
235.461
236.566
237.67
238.774
239.877
240.981
242.084
243.187
244.29
245.392
246.494
247.596
248.698
0.01
238.266
239.386
240.505
241.623
242.742
243.86
244.977
246.095
247.212
248.329
249.445
250.561
251.677
252.793
253.908
255.023
0.005
243.959
245.091
246.223
247.354
248.485
249.616
250.746
251.876
253.006
254.135
255.264
256.393
257.521
258.649
259.777
260.904
0.002
250.977
252.124
253.271
254.418
255.564
256.71
257.855
259.001
260.145
261.29
262.434
263.578
264.721
265.864
267.007
268.149
0.001
255.976
257.135
258.292
259.45
260.607
261.763
262.92
264.075
265.231
266.386
267.541
268.695
269.849
271.002
272.155
273.308
Point Estimates for Reliability at Specified Time
with Confidence Intervals
Example
•
•
•
•
N=100
 = 0.1
Evaluate at 10 hours
R(10)= 0.36
Reliability
Expression for
Exponential
Distribution
Evaluate at 10 hours
R(10)= 0.36
Fixed Truncated Test
2-sided 90% Confidence Intervals
Lower One
Sided
Confidence
Bound
2(866)
c (0.05,194)
2
2(866)
227.496
Two Sided
Confidence
Bound
2(866)
c (0.95,192)
2
2(866)
160.994
7.61
10.758
0.268
0.395
R(10) =
R(10) =
Point Estimate for MTTF with Confidence Intervals
Example: Calculate MTTF with Confidence Intervals for Fixed
Number of Failures on 10 units, C=90%
Fixed Number of Failures
Lower One
Sided
Confidence
Interval
Two Sided
Confidence
Interval
n = number of items placed on test at time t = 0
t* = time at which the life test is terminated
r = number of failures accumulated at time t*
r* = preassigned number of failures
a = acceptable risk of error
1 - a = confidence level
T = total test time
MTTF =
961 =96.1 hrs
10
Total Test Time
Number of Failures

= 1-CL

= 1-0.9 = 0.1
/2 = 0.05
r = 10
2r = 20
Lower One Sided Confidence Bound
2T
1922
c2(0.1, 20)
28.412
2T
2T
c2(0.05, 20)
c2(0.95, 20)
1922
1922
31.41
10.851
(61.19, 177.12)
(67.6, )
Two Sided
Confidence
Bound
Failure
Number
1
2
3
4
5
6
7
8
9
10
Total
Operating
Time (Hrs)
8
20
34
46
63
86
111
141
186
266
961
2T
1922
Chi Square Distribution Table
10.85081 =CHIINV(0.95,20)
P
DF
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
0.995
0.975
3.93E-05 0.000982
0.01
0.0506
0.0717
0.216
0.207
0.484
0.412
0.831
0.676
1.237
0.989
1.69
1.344
2.18
1.735
2.7
2.156
3.247
2.603
3.816
3.074
4.404
3.565
5.009
4.075
5.629
4.601
6.262
5.142
6.908
5.697
7.564
6.265
8.231
6.844
8.907
7.434
9.591
8.034
10.283
8.643
10.982
9.26
11.689
9.886
12.401
10.52
13.12
0.2
1.642
3.219
4.642
5.989
7.289
8.558
9.803
11.03
12.242
13.442
14.631
15.812
16.985
18.151
19.311
20.465
21.615
22.76
23.9
25.038
26.171
27.301
28.429
29.553
30.675
0.1
2.706
4.605
6.251
7.779
9.236
10.645
12.017
13.362
14.684
15.987
17.275
18.549
19.812
21.064
22.307
23.542
24.769
25.989
27.204
28.412
29.615
30.813
32.007
33.196
34.382
0.05
3.841
5.991
7.815
9.488
11.07
12.592
14.067
15.507
16.919
18.307
19.675
21.026
22.362
23.685
24.996
26.296
27.587
28.869
30.144
31.41
32.671
33.924
35.172
36.415
37.652
0.025
5.024
7.378
9.348
11.143
12.833
14.449
16.013
17.535
19.023
20.483
21.92
23.337
24.736
26.119
27.488
28.845
30.191
31.526
32.852
34.17
35.479
36.781
38.076
39.364
40.646
0.02
5.412
7.824
9.837
11.668
13.388
15.033
16.622
18.168
19.679
21.161
22.618
24.054
25.472
26.873
28.259
29.633
30.995
32.346
33.687
35.02
36.343
37.659
38.968
40.27
41.566
0.01
6.635
9.21
11.345
13.277
15.086
16.812
18.475
20.09
21.666
23.209
24.725
26.217
27.688
29.141
30.578
32
33.409
34.805
36.191
37.566
38.932
40.289
41.638
42.98
44.314
0.005
7.879
10.597
12.838
14.86
16.75
18.548
20.278
21.955
23.589
25.188
26.757
28.3
29.819
31.319
32.801
34.267
35.718
37.156
38.582
39.997
41.401
42.796
44.181
45.559
46.928
0.002
9.55
12.429
14.796
16.924
18.907
20.791
22.601
24.352
26.056
27.722
29.354
30.957
32.535
34.091
35.628
37.146
38.648
40.136
41.61
43.072
44.522
45.962
47.391
48.812
50.223
0.001
10.828
13.816
16.266
18.467
20.515
22.458
24.322
26.124
27.877
29.588
31.264
32.909
34.528
36.123
37.697
39.252
40.79
42.312
43.82
45.315
46.797
48.268
49.728
51.179
52.62
Procedure for Calculating Point Estimates
and Confidence Intervals
IEC 60505-4
Statistical Procedures for
Exponential DistributionPoint Estimates,
Confidence Intervals,
Prediction Intervals and
Tolerance Intervals
IEC Tools For Reliability Assessment
IEC 60300-3-5
Reliability Test Conditions and
Statistical Test Principles
IEC 11453
IEC 60605-4
IEC 11453
Point Estimate and Confidence
Intervals for the Binominal
Distribution
IEC 60605-6
IEC 60605-4
Point Estimate and Confidence
Intervals for the Exponential
Distribution
IEC 61649
Point Estimate and Confidence
Intervals for the Weibull Distribution
IEC 61649
IEC 61164
Important Distributions
Exponential
•
•
•
•
Constant Failure Rate
Mixed Failure Modes
Most Electronics
Mean Life R(t)= 0.368
Normal
•
•
•
Wear-out
Greater than 20 samples
Mean Life R(t)= 0.5
Weibull
•
•
•
Can model a variety of
different data types
Infant mortality, constant
failure rate, or wear-out.
Good for limited samples
Weibull Analysis







Method for representing and interpreting data
Provides a Reliability metric directly from plot
Works well with small samples – life data
(failures) provide more information
 (shape or slope),  (characteristic life or scale),
(location or offset)
 < 1 indicates infant mortality
 = 1 indicates random failures
 > 1 indicates wear out failures
Weibull Analysis
1.
2.
3.
4.
Failure
Number
1
2
3
4
5
6
7
8
9
10
Acquire accurate time to failure data
Rank the data first failure to last
Plot the data on Weibull paper
Interpret the plot
1.
2.
3.
4.
Look for mixed modes
Measure slope to determine 
Determine characteristic life 
Read R(t)
Operating
Time
(Hours)
8
20
34
46
63
86
111
141
186
266
Median Rank Table
Rank Order
1
2
3
4
5
6
7
8
9
10
1
50.00
2
29.29
70.71
3
20.63
50.00
79.37
4
15.91
38.57
61.43
84.09
Sample Size
5
6
12.94
10.91
31.38
26.44
50.00
42.14
68.62
57.86
87.06
73.56
89.09
7
9.43
22.85
36.41
50.00
63.59
77.15
90.57
8
8.30
20.11
32.05
44.02
55.98
67.95
79.89
91.70
9
7.41
17.96
28.62
39.31
50.00
60.69
71.38
82.04
92.59
10
6.70
16.23
25.86
35.51
45.17
54.83
64.49
74.14
83.77
93.30
Median Rank Estimate
MR = (i-0.3) *100
(N+0.4)
i= rank order #
N=sample size
Median
Rank 10
Samples
6.70
16.23
25.86
35.51
45.17
54.83
64.49
74.14
83.77
93.30
MTTF vs. MTBF
Start

Failure
MTTF- Mean Time To Failure




16hrs
Expected time to fail for a non-repairable system
Non-repairable systems can fail only once.
MTTF is equivalent to the mean of its failure time distribution.
Ex 16+12+14+6+8 = 56/5= 11.2
12hrs
14hrs
6hrs
8hrs
MTBF- Mean Time Between Failure
16hrs
Time Between Failures
12hrs
14hrs
6hrs
Repair/Restore
Repair/Restore
Repair
Time
Repair/Restore

Repair/Restore

Expected time to fail for repairable systems
Expected time between two consecutive failures for a repairable system
MTBF= MTTF +MTTR
Repair/Restore

Repair/Restore

8hrs
Operational
Non-Operational
90
Rel Freq
(Exp)
0.20
0.16
0.13
0.10
0.08
0.07
0.05
0.04
0.03
0.03
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.0
Exponential PDF
Exponential
80
0.18
70
0.16
60
0.14
Probability Density
400
100
0
90
0.1
199.80
100
227.09
235.08
167.36
Failure
Rate
(Exp)
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
50
40
30
0.12
0.1
y = 0.2e-0.2x
0.08
0.06
20
0.04
10
0.02
0
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Hours
0
10
20
30
Hours
0.25
Exponential CDF & Reliability Function
Exponential (Relative Frequency)
1.2
0.20
1
0.15
y = 0.25e-0.223x
0.10
0.05
0.8
0.6
0.4
0.2
0
0.00
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Hours
Lower Single-Sided Confidence Limit at 90%
3.5
Lower 2-Sided Confidence Limit at
90 %
3.4
Cumulative Density Function
Reliability Function
Total Accumulated Test Time (T)
Number of Failures
Number of Suspensions
Confidence Limit
Alpha
2r
r
Lower Single-Sided
Lower 2-Sided
Upper 2-Sided
Cumul
Time
(Exp)
80
144
195
236
269
295
316
333
346
357
366
373
378
382
386
389
391
393
394
395
396
397
398
398
398
399
399
399
399
400
400
Counts
100
20
Cumul
Failure
(Exp)
20
36
49
59
67
74
79
83
87
89
91
93
95
96
96
97
98
98
99
99
99
99
99
100
100
100
100
100
100
100
100
Relative Frequency
Total Qty At Start of Test
% Failure Rate (Exponential)
Remaining
Interval
Units
Failures
(Hour)
(Exp)
(Exp)
1
80
20
2
64
16
3
51
13
4
41
10
5
33
8
6
26
7
7
21
5
8
17
4
9
13
3
10
11
3
11
9
2
12
7
2
13
5
1
14
4
1
15
4
1
16
3
1
17
2
1
18
2
0
19
1
0
20
1
0
21
1
0
22
1
0
23
1
0
24
0
0
25
0
0
26
0
0
27
0
0
28
0
0
29
0
0
30
0
0
31
0
0
Total
MTTF
4.0
Upper 2-Sided Confidence Limit at
90%
4.8
0
-0.2
5
10
15
20
25
30
Reliability Bathtub Curve
Exponential Distribution
 = failure rate = constant
Weibull Distribution
 < 1 indicates infant mortality
 = 1 indicates random failures
 > 1 indicates wear out failures
System Reliability


Rsystem = Rsubsystem1 *Rsubsystem2 *Rsubsystem3… *Ri
Block Diagrams

Series
.97

.93
.98
.95
.93
.98
.95
R System = 0.84
Parallel-Series
.97
.93
R System = 0.90
Success Run Test to Establish R & C








Success Run Test, Test to a Bogey
Based on a Binomial Distribution
Test Results are Either Success or Failure
Prove a Target Reliability with an assigned Confidence Level
Don’t care about continuous measurement or calculating a parametric value, ie
MTTF or failure rate
Define the test conditions to represent 1 or more lives
Operate without failure for a specified time
Reliability, Confidence, and Sample Size related by Success Run Formula

N= Sample Size, R= Reliability, C= Confidence Level
Success Run Test to Establish R & C

Calculate required number of samples based on R and C

Example
R= 97%, C= 50%



Example
R= 97.7%, C= 90%
Binomial Distribution
Nomograph for
R/C/N
HALT/HASS and Accelerated Testing
Success Run Test to Establish R & C
Define “One Life”

R = 97.7%, C = 90%, N=100

8 hours/day x 365 days/year x 5 years = 14600hrs

Test 24 hours/day 14600/24 = 608 hrs on test

Apply Arrhenius model for
Temperature Acceleration TAF= 11.5

Time on Test = 53 hours
Ea= 0.8eV = 1.28 x 10-19 J
k = 1.38 x 10-23J/K
Tmax = +50C
Ttest= +85C
Time Dependent Failure Mechanisms
OverstressESD, Mechanical Shock, Thermal Breakdown
Time Dependent- Fatigue, Wear, Corrosion
Failure Mode
Failure Mechanism
Accelerating Factors
Loss of signal
Silicon Diffusion
Temperature
Power Failure
Dielectric Breakdown
Electric Field
Loss of signal
Electromigration
Temperature & Power Cycling
Intermittent Output
Corrosion & Oxidation of Fractures
Humidity, Voltage, Temperature
Loss of signal
Dendrite Growth
Humidity, Temperature
Water Intrusion
Seal Leaks
Pressure
Cracked Solder Joint
Fatigue
Thermal cycling & vibration
Accelerated Stress Testing
Acceleration Factors
o
Temperature
o
o
Humidity
o
o
o
Arrhenius Model
Lawson Model
Coffin-Manson
Vibration
o
Power Law and Miner
Criteria
m= S-N slope
o
Voltage
o
o
Inverse Power Law
Product Life Cycling
o
o
CALT Testing
Test to Failure & Apply
Weibull Analysis
MIL-HDBK-338 Table 8-7.1
Product Life Cycling

Calibrated Accelerated Life Testing (CALT)


Suggest primary fatigue mechanism
Simulate loads at three stress levels






90% of foolish load (first test)
80% of first test load
Third stress level

Depends on first two and ultimate life
Test all units to failure
Plot S-N curve, Determine AF’s
Generate Weibull Plot
Product Life Cycling
Accelerated Life Testing
•“Accelerated Testing: Statistical
Models, Test Plans, and Data Analysis”
•By Wayne Nelson
•CALT GMW 8758
•Example
Automatic Lubricating System
CALT Test Example
•Simulate loads
at three stress
levels
•Monitor test
counting cycles
to failure
CALT Test Example
Stress
36
36
36
36
31
31
31
31
25
25
25
25
•Collect Failure Data
•AF = (Saccel/Snormal)b
100000
Cycles to Failure
•Plot and determine Inverse
Power Relationship
Cycles To Failure
3121
1075
629
9452
11386
1104
6624
1577
11044
15405
19257
28723
Pump S-N Curve
10000
1000
-5.93
y = 3050953219559.39x
100
10
Determine AF's
Condition
High Stress
Mid Stress
Confirm Stress
Normal Stress
100
Applied Stress (PSI)
Stress Value (PSI)
36
31
25
15
Accel Factor
180
74
21
N/A
CALT Test Example
Stress Level


Sort and apply median ranks
Generate Weibull Plot
High (IG)
High (IG)
High (IG)
High (PP)
Medium (PP)
Medium (IG)
Medium (PP)
Medium (IG)
Confirm (PP)
Confirm (PP)
Confirm (PP)
Confirm (PP)
Test Stress
3121
1075
629
9452
11386
1104
6624
1577
11044
15405
19257
28723
Accel Factor
180
180
180
180
74
74
74
74
21
21
21
21
Sorted Least to Most
(Resort these numbers for each
change to spreadsheet)
81757
113059
116785
193224
228397
318585
398246
490540
560979
594009
843189
1698933
Median Rank
5.61
13.60
21.67
29.76
37.85
45.95
54.05
62.12
70.24
78.33
86.40
94.39
Cycles at Normal
Stress
560979
193224
113059
1698933
843189
81757
490540
116785
228397
318585
398246
594009
Rank
9
4
2
12
11
1
8
3
5
6
7
10
CALT Test Example
Weibull Plot
•Obtain distribution parameters
•Reliability metrics
•B1, B10
•Reliability vs life
HALT/HASS and Accelerated Testing
Vibration Testing Techniques
Servo-Hydraulic
Electro-Dynamic
• Frequency Range 0.5Hz-300Hz
• Programmable vibration
characteristics; Sine, Random, Sine-
• Frequency Range 3Hz-2,500Hz
• Programmable vibration
characteristics; Sine, Random, Sine-on-
on-Random, Random-on-Random, Field
Data Replay, Mechanical Shock
• Displacement generally up to 12” p-p
• Multi-axis motion from multiple cylinders
Random, Random-on-Random, Field
Data Replay, Mechanical Shock
• Displacement generally limited to 2-3” p-p
• Single axis motion
Repetitive Shock
• Frequency Range 20Hz-10,00Hz
• Vibration output quasi-random with
limited PSD shaping
• Six-axis simultaneous vibration
• High G peak levels
• Displacement generally limited to 0.5”
Electro-Dynamic Vibration Machine
Thermotron armature and
cut-away illustration here???
Armature
Body
Thrust
Armature
Field
Current
Field coil
Center Pole
Base
Vibration Time & Frequency Domain
0.1
G2/Hz
Power
Spectral
Density
(PSD)
Hz
2
1
G pk
50
Hz
2
G pk
1
100
Hz
Random Vibration
Probability Density Function
 = Grms
1 accelerations occur 68% of the time
2 accelerations occur 27% of the time
3 accelerations occur 4% of the time
>3 accelerations occur less than 1% of the time
-5 -4 -3 -2 -
o
 2 3 4 5
Random Vibration

Power Spectral Density Plots

Which is the more severe test?
0.1
G2/Hz
0.2
0.2
Hz
0.2
0.1
0.1
G2/Hz
G2/Hz
0.2
0.2
Hz
0.2
0.2
0.2
Hz
0.2
Power Spectral Density Plots
Vibration Testing






Vibration fatigue failures are caused by stress reversals
Vibration at resonance amplifies damage
High accelerations generate proportional Displacements,
Velocities, and Forces, and damage
A higher concentration of High G peak accelerations has the
potential for greater damage
Most ED vibration testing limits peak accelerations to 3-sigma
RS vibration generates a greater proportion of High G peak
accelerations
HALT/HASS and Accelerated Testing
Which Tests To Run




Input from all departments
Determine failure modes (FMEA)
Consider complete life cycle of product
Suggest stresses that will precipitate failures



Maximum Stress vs Time Dependent
Develop test plan
Execute test
Failure of Electronic Equipment

20 year U.S. Air Force Study



55% of failures due to high temperature and thermal
cycling
20% of failures due to vibration and shock
20% due to humidity
New Product Development Testing Screens
New
Product
HALT, HAST, ESD,
Power Cycle, EMI
RTCA DO-160
MIL-810,
SAE J1455
Temp, Vibration, Shock,
Waterproofness,
Altitude, Humidity
HASS
Analysis
Phase
Development
Phase
Qualitative Testing
Qualification
Testing
Qual Retest
Quantitative Testing
Manufacturing
Screen
Essential Reliability Reference Documents






Vibration Analysis For Electronic Equipment, David S. Steinberg, Third Edition
MIL-HDBK-338B Oct 1998 Military Handbook- Electronic Reliability Design Handbook
The New Weibull Handbook, R.B. Abernathy
Practical Reliability Engineering, Patrick O'Connor
IEC 60300-3-5 Reliability Test Conditions and Statistical Test Principles
GMW 3172:2010
HALT/HASS and Accelerated Testing
HALT and Relaibility Workshop
Any Questions?
Thank You!