Transcript Reliability - Greater El Paso Section ASQ

Reliability
Extending the Quality Concept
Kim Pries

ASQ

 CQA
 CQE
 CSSBB
 CRE

APICS
 CPIM

Director of Product
Integrity & Reliability
for Stoneridge TED
Background in
metallurgy &
materials science
Summary Slide
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What is reliability?
Reliability data
Probability distributions
Most common distribution
Weibull mean
Citation
Shapes of Weibull
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Scale of Weibull
Location of Weibull
Gamma distribution
Non-parametric data fit
What is reliability?
Reliability is the “quality concept” applied
over time
 Reliability engineering requires a different
tool box

Reliability data

Nearly always “units X to failure,” where
units are most often
 Miles
 Hours
(days, weeks, months)
Probability distributions

Exponential
 “Random
failure”
Log-normal
 Weibull
 Gamma

Most common distribution
Equation

Weibull distribution
eta = scale parameter,
beta = shape parameter (or slope),
gamma = location parameter.
Weibull mean
Also known as MTBF or MTTF
 Need to understand gamma function

 1

m ean       
 1


 n 


e
0
x
x
n 1
dx
Citation
Using diagrams from Reliasoft Weibull++
7.x
 A few from Minitab

Shapes of Weibull
Scale of Weibull
Location of Weibull
Gamma distribution
ReliaSoft Weibull++ 7 - www.ReliaSoft.com
Probability - Gamma
99.990
Probability-Gamma
U n r e lia b ilit y , F ( t )
Folio1\Data 1
Gamma-2P
RRX SRM MED FM
F=2986/S=0
Data Points
Probability Line
50.000
10.000
5.000
0.500
0.010
9/5/2006
6:58:57 AM
6.000
404.800
803.600
1202.400
Time, (t)

1601.200
2000.000
Non-parametric data fit
Empirical data fit
Weibull
Failure to time
100
Shape
3.368
Scale
23.57
N
Pe rce nt
80
60
40
20
0
5
10
15
20
25
Months to failure
30
35
40
514
Summary Slide
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Accelerated life testing
Accelerated Life Testing
Highly accelerated life
testing
Multi-environment
overstress
MEOST, continued
Step-stress
HASS and HASA
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Achieving reliability
growth
Reliability Growth-Duane
Model
Reliability GrowthAMSAA model
Accelerated life testing
ReliaSoft ALTA 6.0 PRO - ALTA.ReliaSoft.com
Probability Weibull
99.00
Arrh/Weib
Data 1
90.00
400
406
F=5 | S=0
416
F=6 | S=0
426
F=6 | S=0
U nreliability
50.00
10.00
5.00
User's Name
Company
9/5/2006 07:01
1.00
10.00
100.00
Time
Beta=2.9658, B=1.0680E+4, C=2.3966E-9
1000.00
Accelerated Life Testing
Can be used to predict life based on
testing
 A typical model looks like

Highly accelerated life testing
No predictive value
 Reveals weakest portions of design
 Examples:

 Thermal
shock
 Special drop testing
 Mechanical shock
 Swept sine vibration
Multi-environment overstress
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Derate components
Study thermal
behavior
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 Scan
 Finite
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
element analysis
Modular designs
DFM
Mfg line ‘escapes’
RMAs

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Robust…high S/N
ratio
Design for
maintainability
Product liability
analysis
Take apart supplier
products
FFRs
MEOST, continued
Test to failure is goal
 Combined stress environment
 Beyond design levels
 Lower than immediate destruct level
 Example:

 Simultaneous
Temperature
 Humidity
 Vibration
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Step-stress
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Cumulative damage
model
Harder to relate to
reality
HASS and HASA
Screening versus sampling
 Small % of life to product
 Elicit ‘infant mortality’ failures
 Example:

 Burn-in
Achieving reliability growth
Detect failure causes
 Feedback
 Redesign
 Improved fabrication
 Verification of redesign

Reliability Growth-Duane Model
ReliaSoft's RGA 6 - RGA.ReliaSoft.com
Cumulative Number of Failures vs Time

Cruder than AMSAA
model
Shows same general
improvement
10000.00
Duane
Data 1
Developmental
LS
1000.00
Cum . Num ber of F ailur es

100.00
10.00
Kim Pries
Stoneridg e TED
9/12/2006 11:01
1.00
100.00
1000.00
Time
Alpha=-1.9467, b=18364.7224
Reliability Growth-AMSAA model
ReliaSoft's RGA 6 - RGA.ReliaSoft.com
Cumulative Number of Failures vs Time
10000.00
Crow-AM SAA (NHPP)
Data 1
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M LE
1000.00
Cum. Number of F ailur es
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100.00
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10.00
User Name
Company
6/22/2006 14:27
1.00
100.00
1000.00
Time
Beta=1.3304, Lambda=0.7674
Cumulative
failures
Initially very
poor
Improves over
time
Summary Slide
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Effects of design
Effects of manufacturing
Can’t we predict?
Warranty
Warranty
Serial reliability
Parallel reliability
(redundancy)
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Other tools
Software reliability
Effects of design
Usually the heart of warranty issues
 Counteract with robust design
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Effects of manufacturing
Manufacturing can degrade reliability
 Cannot improve intrinsic design issues
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Can’t we predict?

MIL-HDBK-217F
 No
parallel circuits
 Electronics only
 Extremely conservative
Leads to over-engineering
 Excessive derating
 Off by factors of at least 2 to 4
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Warranty
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1-dimensional
 Example:

miles only
2-dimensional
 Example:
Miles
 Years
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Warranty
Non-renewing
 Pro-rated
 Cumulative
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 Multiple

items
Reliability improvement
Serial reliability
Simple product of the probabilities of
failure of components
 More components = less reliability

n
seria l relia b ility 

i 1
xi
Parallel reliability (redundancy)

Dramatically reduces probability of failure
n
parallel reliability  1   (1  x i )
i 1
Other tools
FMEA
 Fault Tree Analysis
 Reliability Block Diagrams
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 Simulation
Software reliability
Difficult to prove
 Super methods
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 B-method
 ITU
Z.100, Z.105, and Z.120
 Clean room
Summary Slide
What about maintenance?
 Pogo Pins
 Pogo Pins (product 1)
 Pogo Pins (Product 2)
 Pogo Pin conclusions
 Preventive vs. Predictive
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What about maintenance?
Same math
 Looking for types of wear and other failure
modes
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Pogo Pins
Probability Density Function
ReliaSoft Weibull++ 7 - www.ReliaSoft.com
0.300
Pdf
Pogo Failures++\Data 1
Weibull-3P
RRX SRM MED FM
F=526/S=0
Pdf Line
0.240
f(t)
0.180
0.120
0.060
Kim Pries
Stoneridge TED
12/12/2005
12:17:15 PM
0.000
0.000

0.800
1.600
2.400
Time, (t)
3.200
4.000
Pogo Pins (product 1)
Distribution Overview Plot for ESC_Pogo
ML Estimates-Complete Data
P robability D ensity F unction
T able of S tatistics
Weibull
S hape
99.9
0.6
90
P DF
P e r ce nt
0.4
0.2
50
4.69196
M ean
6.08597
S tD ev
9.16024
M edian
2.74296
IQ R
6.81390
F ailure
10
C ensor
A D*
0.0
0
15
30
ESC _P ogo
1
45
0.01
S urv iv al F unction
100.00
H azard F unction
100
0.6
0.4
R a te
P e r ce nt
0.10
1.00
10.00
ESC _P ogo
50
0.2
0
0.0
0
15
30
ESC _P ogo
45
0
15
30
ESC _P ogo
45
0.682757
S cale
138
0
5.296
Pogo Pins (Product 2)
Distribution Overview Plot for 4WD_Pogo
ML Estimates-Complete Data
P robability D ensity F unction
T able of S tatistics
Weibull
S hape
99.9
0.6
90
P DF
P e r ce nt
0.4
0.2
50
5.25305
M ean
7.32163
S tD ev
11.9253
M edian
2.95918
IQ R
8.01387
F ailure
10
C ensor
A D*
0.0
0
20
40
4 WD_P ogo
1
0.001
60
0.100
1.000
10.000
100.000
4 WD_P ogo
S urv iv al F unction
H azard F unction
100
0.6
0.4
R a te
P e r ce n t
0.010
50
0.2
0
0.0
0
20
40
4 WD_P ogo
60
0
20
40
4 WD_P ogo
60
0.638638
S cale
96
0
3.925
Pogo Pin conclusions
Very quick “infant mortality”
 Random failure thereafter
 Difficult to find a nice preventive
maintenance schedule
 Frequent inspection

Preventive vs. Predictive
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Preventive maintenance
 Fix
before it breaks
 Statistically based intervals
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Predictive maintenance
 Detect
anomalies
 Always uses sensors
The future
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Combinatorial testing
 Designed
experiments
Response surfaces
 Analysis of variance
 Analysis of covariance
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Eyring models
 Multiple
environments