2012 ARS, Europe: Warsaw, PolandTrack 1, Session 5
Begins at 9:10 AM, Thursday, March 29th
Why You Cannot PredictElectronic Product Reliability
Albertyn BarnardLambda Consulting
LambdaConsulting
PRESENTATION SLIDESPRESENTATION SLIDESThe following presentation was delivered at the:
International Applied Reliability Symposium, EuropeMarch 28 - 30, 2012: Warsaw, Polandhttp://www.ARSymposium.org/europe/2012/
The International Applied Reliability Symposium (ARS) is intended to be a forum for reliability and maintainability practitioners within industry and government to discuss their success stories and lessons learned regarding
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delivered at the Symposium are selected on the basis of the presentation proposals received.
Although the ARS may edit the presentation materials as needed to make them ready to print, the content of the presentation is solely the responsibility of the author. Publication of these presentation materials in the
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The publication of these materials in the ARS presentation format is Copyright © 2012 by the ARS, All Rights Reserved.
Albertyn Barnard, Lambda Consulting Slide Number: 2Session 5Track 1
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AgendaAgenda
Introduction 5 min
What is reliability? 5 min
Why you cannot predict reliability 25 min
Published failure data
When can reliability prediction be used? 10 min
Practical prototype test
Physics of failure analysis
Summary 5 min
Questions 10 min
Albertyn Barnard, Lambda Consulting Slide Number: 3Session 5Track 1
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IntroductionIntroduction
Albertyn Barnard, South Africa
Reliability engineering consultant since 1982
Primary focus on electronic product development
Systems engineering viewpoint
Established first commercial HALT facility in South Africa
Why you cannot predict electronic product reliability
What is reliability prediction?
What is reliability engineering?
What is reliability accounting?
Albertyn Barnard, Lambda Consulting Slide Number: 4Session 5Track 1
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IntroductionIntroduction
An accurate prediction of the field reliability of an electronic product during the development stage is, for obvious reasons, highly desirable:
Accurate forecasts of support requirements
Spares, facilities, personnel, etc.
Accurate forecasts of financial risks
Annual return rate, warranty costs, etc.
Marketability benefits
Many reliability prediction standards have been developed and applied for many years, and some “new” standards are constantly under development
However, when these methods and standards are carefully analysed,all seem to be based on misleading or even incorrect assumptions
Albertyn Barnard, Lambda Consulting Slide Number: 5Session 5Track 1
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IntroductionIntroduction
This presentation argues that reliability prediction of an electronic product as performed today in many industries is an exercise in futility
All design engineers and technical managers should be aware of these serious shortcomings
The presentation concludes with an example on when reliability prediction may provide useful engineering knowledge
Objective of reliability prediction:
To estimate field reliability (during product development stages)
Development & Production Operations
Futuret=0
Albertyn Barnard, Lambda Consulting Slide Number: 6Session 5Track 1
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IntroductionIntroduction
Basic reasoning when performing reliability prediction:
Product consists of parts
Parts have failure rates
Determine part failure rates
Add part failure rates to obtain product failure rate
Experience suggests that some products never fail (in useful life),while others fail frequently
Why are some products more reliable than others,especially since basically the same parts are used?
Consider the following scenario:
Product contains 2,000 electronic parts
When a failure occurs and root cause analysis is performed, system failure can usually be attributed to the failure of a single part (i.e. 1,999 parts not failed)
System “MTBF” is then calculated based on the reliability of this single part?
Albertyn Barnard, Lambda Consulting Slide Number: 7Session 5Track 1
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All failures in electronic equipment can be attributed to a traceable and preventable cause, and may not be satisfactorily explained as the manifestation of some statistical inevitability.
Norman Pascoe
Reliability Technology : Principles and Practiceof Failure Prevention in Electronic Systems, 2011
What is reliability?What is reliability?
All non-conformances are caused.Anything that is caused can be prevented.
Philip Crosby
Quality Without Tears:The Art of Hassle-Free Management, 1995
Albertyn Barnard, Lambda Consulting Slide Number: 8Session 5Track 1
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What is reliability?What is reliability?
These quotations emphasise two fundamental concepts in reliability engineering:
1) failures are caused, and2) failures can be prevented
Reliability is the absence of failuresReliability engineering is the management function
that prevents the creation of failures
Development & Production Operations
Futuret=0
Albertyn Barnard, Lambda Consulting Slide Number: 9Session 5Track 1
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What is reliability?What is reliability?
Product is reliable if it does not fail!
This is what the customer expects!
Failure-free state can only be achieved if failure is prevented from occurring
What is required to prevent failures?
Engineering knowledge to understand failure mechanisms
Management commitment to mitigate or eliminate them
Proactive prevention should be the focus of reliability engineering
Not reactive failure correction or failure management
Reliability engineering should not be “playing the numbers game”
Failures are created primarily due to errors made by design and production personnel
Products seldom fail due to part failure
Products often fail due to incorrect application and integration of those parts
Albertyn Barnard, Lambda Consulting Slide Number: 10Session 5Track 1
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What is reliability?What is reliability?
Time
Failu
re ra
te
Externally induced failuresFailure of weak items
Wear-out failures
Infant mortality Useful life Wear-out
Bathtub curve
Albertyn Barnard, Lambda Consulting Slide Number: 11Session 5Track 1
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What is reliability?What is reliability?
Time
Failu
re ra
te
No or low infant mortality
No or low failures during longer useful life
Wear-out occurs later
Infant mortality Useful life Wear-out
Improved bathtub curve
Albertyn Barnard, Lambda Consulting Slide Number: 12Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction based on “published failure data”
System or product decomposition
Obtain failure rate for each part (assuming all parts have failure rates)
Calculate part failure rate (based on Arrhenius model, for temperature),and number of Pi factors (e.g. environment, quality, complexity, etc.)
Use database (similar item, parts count, part stress)
Add failure rates for system failure rate (assuming failure rates can be added)
MTBF = 1 / Σ λi
MIL-HDBK-217"Reliability Prediction of Electronic Equipment”
Most widely used approach by both commercial and defence
No longer being updated by US DoD
Albertyn Barnard, Lambda Consulting Slide Number: 13Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction based on “published failure data”
BELLCORE TR-332 (Telcordia SR-332)Telecommunications industry
RDF 2000European method developed by CNET
217PlusReliability Information Analysis Center
HDR5British Telecom
IEC 61709 & IEC TR 62380 (Reliability data handbook)Electric components – Reliability – Reference conditionsfor failure rates and stress models for conversion
Albertyn Barnard, Lambda Consulting Slide Number: 14Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction based on “published failure data”
MIL-HDBK-217 "Reliability Prediction of Electronic Equipment”
Comment published 44 years ago:
“Figures 4.5 to 4.14 are adapted from “Reliability Stress and Failure Rate Data,” Mil-Hdbk-217, Government Printing Office, Washington, D.C., 1962. The second edition bears the number Mil-Hdbk-217A, and was published in 1965. It is disquieting that in many cases 217A (based on different but supposedly equivalent data) tabulates failure rates a decade higher than 217. Not only is the magnitude of the difference significant, but the direction is counter to the trend which one would expect during a time of component-reliability improvement.”
Martin ShoomanProbabilistic Reliability : An Engineering Approach, McGraw-Hill, 1968
Albertyn Barnard, Lambda Consulting Slide Number: 15Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction based on “published failure data”
Reliability prediction is exercise in futility!
Calculated MTBF = 2,204,750 hours(for GB, 21ºC)
2,204,750 hours = 251 years!
This is not (reliability) engineering!
http://ultravolt.com
Albertyn Barnard, Lambda Consulting Slide Number: 16Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction based on “published failure data”
Max rated temperature Operating temperature
Fai
lure
rat
e
Reality
Mil-Hdbk-217F
Albertyn Barnard, Lambda Consulting Slide Number: 17Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction based on “published failure data”
A rough rule of thumb is that the operating life of semiconductor devices decreases by half for every 10°C rise in temperature above 100°C.
Article in Nuts and Volts (July 2009), reference Motorola Semiconductor Technical Data Sheet AN1083, 1990
Albertyn Barnard, Lambda Consulting Slide Number: 18Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Some well-known documents such as Mil-Hdbk-217 and derivatives of it treat all flaws as being precipitated by temperature alone, which is completely erroneous. As a matter of general interest, it is noted in passing that the Arrhenius equation has been incorrectly used to describe any number of failure modes which do not follow the equation at all. Mil-Hdbk-217 was a prime example of the rampant misuse of the Arrhenius equation.
Gregg HobbsAccelerated Reliability Engineering: HALT & HASS, 2000
Albertyn Barnard, Lambda Consulting Slide Number: 19Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
In the author's opinion, Mil-Hdbk-217 should be immediately placed in the shredder and all concepts there from simultaneously placed in one's mental trash can. Mil-Hdbk-217 will go down in history as one of the biggest impediments to progress ever promulgated on the technical community.
Gregg HobbsAccelerated Reliability Engineering: HALT & HASS, 2000
Albertyn Barnard, Lambda Consulting Slide Number: 20Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
PDT O’ConnorSolid State Technology, August 1990
A very serious reservation arises in connection with the relationship between temperature and failure rate expressed by the reliability predictions of Mil-Hdbk-217. The usual relationship is based on the Arrhenius formula for reaction kinetics in physics and chemistry.
The relationships in electronic devices have been worked out by testing parts to failure at high temperatures and by calculating the activation energies for the processes which lead to failure. The flaw in this argument is that the great majority of electronic parts do not suffer from physical or chemical degradation.
Albertyn Barnard, Lambda Consulting Slide Number: 21Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
CT Leonard IEEE Transactions on Reliability, December 1988
Temperature is probably simply another design variable, and onceaccommodated by engineering techniques, would have no other influence,i.e. reduction in temperature would not reduce failures.
It is probably a lot more cost-effective to design boxes for the environment than to modify the environment to suit perceived sensitivities, especially when those sensitivities are at best vaguely understood.
Albertyn Barnard, Lambda Consulting Slide Number: 22Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
EB Hakim Solid State Technology, August 1990
It is my own belief that under worst case design operating conditions for equipment, temperature induced failure mechanisms are not significant during the useful life of a system.
For this to be true, a necessary condition is that the electrical functionality of system components is assured beyond the system temperature envelope. The significance of this is that system reliability will not be improved by lowering the equipment operating temperature.
Albertyn Barnard, Lambda Consulting Slide Number: 23Session 5Track 1
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If you can predict reliability, why don’t you prevent failures?
An accurate prediction of reliability implies such knowledge of the cause of failure that they could be eliminated
If you can predict reliability, it means that you know what will fail in future.Why not prevent it from occurring now?
Why you cannot predict reliabilityWhy you cannot predict reliability
Albertyn Barnard, Lambda Consulting Slide Number: 24Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction is contrary to proven wisdom expressed by qualityand reliability gurus
Edwards Deming: “Avoid numerical goals. Alternatively, learn the capabilities of processes, and how to improve them.”
Philip Crosby: “Zero Defects” is an asymptote (i.e. continuous improvement).”
Ralph Evans: “The ultimate goal of reliability engineering is surely not to generate an accurate reliability number for the item.”
If the reader is to play an effective role in contributing to failure-free targets, then it is vital that the myths embedded within much of the twentieth century reliability folklore are properly recognised and appropriately discarded. On the other hand, the legacies bequeathed by the quality pioneers and gurus of the twentieth century should, based upon their proven merit, be studied, understood and applied with earnest enthusiasm. Norman Pascoe
Albertyn Barnard, Lambda Consulting Slide Number: 25Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Since failures are caused by people, why allocate failure rates to parts?
Failures are primarily caused by errors made by design and production personnel
Failures due to human nature and complexity of engineering
Success depends on an awareness of all possible failure modes, and whenever a designer is either ignorant of, or uninterested in, or disinclined to thinkin terms of failure, he can inadvertently invite it.
Ivars PetersonVintage Books, 1996
Albertyn Barnard, Lambda Consulting Slide Number: 26Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Many parts do not have a property such as “failure rate”
Many electronic part failures are caused by mechanical failure mechanisms (environment)
Vibration (inferior mechanical design (e.g. natural frequency))
Temperature (inferior thermal design (e.g. exceeding thermal envelope))
Albertyn Barnard, Lambda Consulting Slide Number: 27Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Parts with “failure rates” may have insignificant failure rates during their useful life
Many products replaced due to technical obsolescence
Datasheet failure rates (e.g. http://www.ti.com)
10.16 FIT = 10.16 x 10-9 hoursMTTF = 9.84 x 107 hours = 11,235 years
MTBF?
Albertyn Barnard, Lambda Consulting Slide Number: 28Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Failures may be caused by software
How do you predict software reliability?
Methods based on number of faults found during testing?
Most prediction methods conveniently ignore software reliability
Most modern products contain one (or many) microcontrollers
Interaction between hardware and software may be highlighted during accelerated testing (e.g. HALT)
Albertyn Barnard, Lambda Consulting Slide Number: 29Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
The failure rate of a system is not the sum of the failure rate of its parts
Series configuration model is invalid
e.g. pull-up vs. filter resistor
Interaction of parts often fails
e.g. without individual part failure, timing, parameter drift
Integration of parts often fails
e.g. without individual part failure, quality of production / assembly
Albertyn Barnard, Lambda Consulting Slide Number: 30Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
All part failures do not have “constant failure rates”
Exponential distribution may be invalid
What is MTBF?
Expected life?
Mean value of a distribution?
Mean value of which distribution?
Reliability Edge, Volume 11, Issue 1, ReliaSoft Corporation
Albertyn Barnard, Lambda Consulting Slide Number: 31Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Accelerated testing will accelerate different failure mechanisms differently
How do you do an accelerated life test on the product level?
Subject product to step-stress test (e.g. temperature)?
What failure mechanisms do you accelerate?
Probably only those failure mechanisms most sensitive to specific stress condition (i.e. activation energy)?
Do you actually measure activation energy, or do you assume a value?
Selected model (e.g. Arrhenius or “Failure rate – temperature relationship”)may be invalid for solid-state electronics
Life of individual parts accelerated at different rates, yet we present results as if every part has been aged during test
Accelerated life testing is very useful for relative comparisons between technologies, parts, etc.
Albertyn Barnard, Lambda Consulting Slide Number: 32Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction results are frequently unrelated to real-life observations
ANSI/VITA 51.1, American National Standard for Reliability Prediction Mil-Hdbk-217 Subsidiary Specification, June 2008
“Manufacturers and electronic reliability engineers use different methods to adjust themodels in MIL-HDBK-217F Notice 2 for newer technologies, use different defaultsfor unknown stress conditions, and make differing assumptions of quality andcomplexity factors for COTS items. These differing methods yield results that are notcomparable. This specification is intended to provide a standard method for reliabilityengineers to perform failure rate predictions for COTS items used in military or highreliability applications.”
Use Pi Q = 1 (and not 10) for commercial integrated circuitsUse voltage ratio = 0.5 as standard default for semiconductors
“This is considered an average setting for the voltage ratio.”
Albertyn Barnard, Lambda Consulting Slide Number: 33Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction results are frequently unrelated to real-life observations
ANSI/VITA 51.1, Reliability Prediction MIL-HDBK-217 Subsidiary Specification,June 2008
This specification provides standard defaults and methods to adjust the models in MIL-HDBK-217F Notice 2. This is not a revision of MIL-HDBK-217F Notice 2 but a standardization of the inputs to the MIL-HDBK-217F Notice 2 calculations to give more consistent results.
ANSI/VITA 51.2, Physics of Failure Reliability Predictions, 2011
It includes a discussion of the philosophy, context for use, definitions, models for key failure mechanisms, definition of the input data required, default values if technically feasible or the typical range of values as a guideline. It defines how modeling results are interpreted and used. It requires the documentation of modeling inputs, assumptions made during the analysis, modifications to the models and rationale for the analysis.
Albertyn Barnard, Lambda Consulting Slide Number: 34Session 5Track 1
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Why you cannot predict reliabilityWhy you cannot predict reliability
Reliability prediction results are frequently unrelated to real-life observations
Many other company proprietary databases
Use field correction factors
Assume only 20% of Mil-Hdbk-217F for FETs
Modify quality levels
Assume high mil-spec quality levels for lower quality parts
It does not make any difference how smart you are, who made the guess,or what his name is – if it disagrees with real-life results, it is wrong.That is all there is to it.
Dr. Richard FeynmanNobel Prize-winning physicist
Albertyn Barnard, Lambda Consulting Slide Number: 35Session 5Track 1
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When can reliability prediction be used?When can reliability prediction be used?
Practical prototype test
“Failure rate measurement and prediction”
System or product step-stress accelerated test
Determine time-to-failure distribution
needs sample of test units
Determine acceleration factor
needs typically three samples tested at different stress levels
http://quanterion.com
Albertyn Barnard, Lambda Consulting Slide Number: 36Session 5Track 1
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When can reliability prediction be used?When can reliability prediction be used?
Practical prototype test
Accelerated Testing: The Only Game in Town
There is the old joke about the gambler who was told that the game he was in was crooked. His reply was, “I know it’s crooked, but it’s the only game in town.”Many of the justifications for certain kinds of accelerated testing remind me of that joke. There are several forms of accelerated testing, but they all try (by definition) to get results when results are not available with ordinary use conditions.
Now, there is nothing wrong with accelerated testing per se. We all do it all the time, and it serves a useful qualitative purpose. But fools (among others) often try to extrapolate quantitatively the accelerated results to ordinary use conditions.
Accelerated tests can help us find failure-modes or failure-resistances that ought to be explored to see if they might occur in ordinary use. But beware of those who justify their procedures by something equivalent to “It’s the only game in town.”
Ralph Evans
IEEE Transactions on Reliability, Vol. R-26, No. 4, October 1977
Albertyn Barnard, Lambda Consulting Slide Number: 37Session 5Track 1
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When can reliability prediction be used?When can reliability prediction be used?
Physics of failure analysis
“Failure mechanism knowledge and prediction”
Physics of failure approach developed from research to understand fundamental failure mechanisms (i.e. not failure modes)
Detailed root cause analysis of field or test failure
Knowledge gained from physics of failure approach being usedproactively to prevent similar failures in new products
Technology is moving from “part level” to “product level”
Technology is moving from “physics of failure” to “reliability physics”
Typical analyses:
Vibration
Shock
Thermal cycling
Solder joint fatigue
Albertyn Barnard, Lambda Consulting Slide Number: 38Session 5Track 1
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When can reliability prediction be used?When can reliability prediction be used?
Physics of failure analysis
Only when failure mechanisms are known and understood
e.g. physics of failure, reliability physics
Only when product may fail due to cumulative damage
e.g. fatigue, wear-out
Only when we predict part reliability (and not system reliability)
Not for infant mortality and “random” failures?
Albertyn Barnard, Lambda Consulting Slide Number: 39Session 5Track 1
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SummarySummary
The Wonderful One-Hoss-Shay
Oliver Wendell Holmes 100 years
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The Wonderful One-Hoss-Shay
Have you heard of the wonderful one-hoss-shay,That was built in such a logical wayIt ran a hundred years to a day,And then, of a sudden, it--ah, but stayI 'll tell you what happened without delay,Scaring the parson into fits,Frightening people out of their wits,--Have you ever heard of that, I say?
You see, of course, if you 're not a dunce,How it went to pieces all at once,--All at once, and nothing first,--Just as bubbles do when they burst.End of the wonderful one-hoss-shay. Logic is logic. That's all I say.
Oliver Wendell Holmes, 1858
SummarySummary
Albertyn Barnard, Lambda Consulting Slide Number: 41Session 5Track 1
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The Wonderful One-Hoss-Shay
Oliver Wendell Holmes
"The Wonderful One-Hoss Shay" is a perfectly intelligible conception, whatever material difficulties it presents. It is conceivable that a being of an order superior to humanity should so understand the conditions of matter that he could construct a machine which should go to pieces, if not into its constituent atoms, at a given moment of the future. The mind may take a certain pleasure in this picture of the impossible.
100 years
SummarySummary
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SummarySummary
Perform reliability prediction based on “published failure data”Worst methodPrediction based on data unrelated to your productExercise in futility
Perform reliability prediction based on “practical prototype test”Better method Prediction based on (limited) evidence of actual product reliabilityCareful of assumptions and conclusions“Only game in town”
Perform reliability prediction based on “physics of failure analysis”Best methodPrediction based on engineering knowledge of failure mechanismsTechnology maturing into practical methods“Reliability physics”
Albertyn Barnard, Lambda Consulting Slide Number: 43Session 5Track 1
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SummarySummary
Perform reliability prediction based on “published failure data”
Perform reliability prediction based on “practical prototype test”
Perform reliability prediction based on “physics of failure analysis”
(Quantification) of reliability is in effect a distractionto the goals of reliability. (e-mail from) Ted Kalal
Albertyn Barnard, Lambda Consulting Slide Number: 44Session 5Track 1
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Where to get more informationWhere to get more information
Patrick O’Connor and Andre Kleyner,Practical Reliability Engineering, 5th edition,John Wiley, 2012
Accelerated Testing:
www.ReliaSoft.com
www.weibull.com
Physics of Failure:Center for Advanced Life Cycle EngineeringUniversity of Marylandwww.calce.umd.edu
Albertyn Barnard, Lambda Consulting Slide Number: 45Session 5Track 1
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Albertyn BarnardAlbertyn Barnard
• M.Eng. (Electronics), M.Eng. (Engineering Management)
• Lambda Consulting
PO Box 11826, Hatfield 0028, South Africa
• Consulting services in reliability engineering
• Commercial HALT facility in Pretoria, South Africa
• Part-time lecturer at Graduate School of Technology Management,University of Pretoria, South Africa
• INCOSE South Africa President 2008
• Chair of INCOSE Reliability Engineering Working Group
• Mobile : +27 82 344 0345
• www.lambdaconsulting.co.za
LambdaConsulting
Albertyn Barnard, Lambda Consulting Slide Number: 46Session 5Track 1
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QuestionsQuestions
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