2012 ARS, Europe: Warsaw, Poland
Track 1, Session 5
Begins at 9:10 AM, Thursday, March 29th
Why You Cannot Predict
Electronic Product Reliability
Albertyn Barnard
Lambda Consulting
Lambda
Consulting
PRESENTATION SLIDES
The following presentation was delivered at the:
International Applied Reliability Symposium, Europe
March 28 - 30, 2012: Warsaw, Poland
http://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
the application of reliability techniques to meet real world challenges. Each year, the ARS issues an open
"Call for Presentations" at http://www.ARSymposium.org/europe/presenters/index.htm and the presentations
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
ARS Proceedings does not imply that the information and methods described in the presentation have been
verified or endorsed by the ARS and/or its organizers.
The publication of these materials in the ARS presentation format is
Copyright © 2012 by the ARS, All Rights Reserved.
Applied Reliability Symposium, Europe 2012
Agenda
Introduction
What is reliability?
Why you cannot predict reliability
Published failure data
When can reliability prediction be used?
Practical prototype test
Physics of failure analysis
Summary
Questions
Albertyn Barnard, Lambda Consulting
Track 1
5 min
5 min
25 min
10 min
5 min
10 min
Session 5
Slide Number: 2
Introduction
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
Applied Reliability Symposium, Europe 2012
Why you cannot predict electronic product reliability
What is reliability prediction?
What is reliability engineering?
What is reliability accounting?
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 3
Applied Reliability Symposium, Europe 2012
Introduction
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
Track 1
Session 5
Slide Number: 4
Introduction
Applied Reliability Symposium, Europe 2012
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
t=0
Albertyn Barnard, Lambda Consulting
Future
Track 1
Session 5
Slide Number: 5
Applied Reliability Symposium, Europe 2012
Introduction
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
Track 1
Session 5
Slide Number: 6
What is reliability?
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
Applied Reliability Symposium, Europe 2012
Reliability Technology : Principles and Practice
of Failure Prevention in Electronic Systems, 2011
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
Track 1
Session 5
Slide Number: 7
What is reliability?
These quotations emphasise two fundamental concepts in reliability engineering:
1) failures are caused, and
2) failures can be prevented
Applied Reliability Symposium, Europe 2012
Reliability is the absence of failures
Reliability engineering is the management function
that prevents the creation of failures
Development & Production
Operations
t=0
Albertyn Barnard, Lambda Consulting
Future
Track 1
Session 5
Slide Number: 8
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 9
What is reliability?
Failure rate
Applied Reliability Symposium, Europe 2012
Bathtub curve
Wear-out failures
Failure of weak items
Infant mortality
Albertyn Barnard, Lambda Consulting
Useful life
Externally induced failures
Wear-out
Track 1
Time
Session 5
Slide Number: 10
What is reliability?
Failure rate
Applied Reliability Symposium, Europe 2012
Improved bathtub curve
Wear-out occurs later
No or low infant mortality
No or low failures during longer useful life
Infant mortality
Albertyn Barnard, Lambda Consulting
Useful life
Track 1
Wear-out
Session 5
Time
Slide Number: 11
Applied Reliability Symposium, Europe 2012
Why 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
Track 1
Session 5
Slide Number: 12
Why you cannot predict reliability
Reliability prediction based on “published failure data”
BELLCORE TR-332 (Telcordia SR-332)
Telecommunications industry
Applied Reliability Symposium, Europe 2012
RDF 2000
European method developed by CNET
217Plus
Reliability Information Analysis Center
HDR5
British Telecom
IEC 61709 & IEC TR 62380 (Reliability data handbook)
Electric components – Reliability – Reference conditions
for failure rates and stress models for conversion
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 13
Why you cannot predict reliability
Reliability prediction based on “published failure data”
MIL-HDBK-217 "Reliability Prediction of Electronic Equipment”
Applied Reliability Symposium, Europe 2012
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 componentreliability improvement.”
Martin Shooman
Probabilistic Reliability : An Engineering Approach, McGraw-Hill, 1968
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 14
Why you cannot predict reliability
Applied Reliability Symposium, Europe 2012
Reliability prediction based on “published failure data”
Reliability prediction is exercise in futility!
http://ultravolt.com
Calculated MTBF = 2,204,750 hours
(for GB, 21ºC)
2,204,750 hours = 251 years!
This is not (reliability) engineering!
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 15
Why you cannot predict reliability
Reliability prediction based on “published failure data”
Applied Reliability Symposium, Europe 2012
Failure rate
Mil-Hdbk-217F
Reality
Max rated temperature
Albertyn Barnard, Lambda Consulting
Track 1
Operating temperature
Session 5
Slide Number: 16
Why you cannot predict reliability
Reliability prediction based on “published failure data”
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 17
Applied Reliability Symposium, Europe 2012
Why you cannot predict reliability
Some well-known documents such as Mil-Hdbk217 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 Hobbs
Accelerated Reliability Engineering: HALT & HASS, 2000
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 18
Why you cannot predict reliability
Applied Reliability Symposium, Europe 2012
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 Hobbs
Accelerated Reliability Engineering: HALT & HASS, 2000
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 19
Why you cannot predict reliability
PDT O’Connor
Solid State Technology, August 1990
Applied Reliability Symposium, Europe 2012
A very serious reservation arises in connection with the relationship between
temperature and failure rate expressed by the reliability predictions of MilHdbk-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
Track 1
Session 5
Slide Number: 20
Why you cannot predict reliability
CT Leonard
IEEE Transactions on Reliability, December 1988
Applied Reliability Symposium, Europe 2012
Temperature is probably simply another design variable, and once
accommodated 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
Track 1
Session 5
Slide Number: 21
Why you cannot predict reliability
EB Hakim
Solid State Technology, August 1990
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 22
Why you cannot predict reliability
If you can predict reliability, why don’t you prevent failures?
Applied Reliability Symposium, Europe 2012
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?
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 23
Why you cannot predict reliability
Reliability prediction is contrary to proven wisdom expressed by quality
and reliability gurus
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 24
Why you cannot predict reliability
Since failures are caused by people, why allocate failure rates to parts?
Applied Reliability Symposium, Europe 2012
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 think
in terms of failure, he can inadvertently invite it.
Ivars Peterson
Vintage Books, 1996
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 25
Why you cannot predict reliability
Many parts do not have a property such as “failure rate”
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 26
Why 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)
Applied Reliability Symposium, Europe 2012
MTBF?
10.16 FIT = 10.16 x 10-9 hours
MTTF = 9.84 x 107 hours = 11,235 years
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 27
Why you cannot predict reliability
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 28
Why you cannot predict reliability
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 29
Why you cannot predict reliability
All part failures do not have “constant failure rates”
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 30
Why you cannot predict reliability
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 31
Why you cannot predict reliability
Reliability prediction results are frequently unrelated to real-life observations
Applied Reliability Symposium, Europe 2012
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 the
models in MIL-HDBK-217F Notice 2 for newer technologies, use different defaults
for unknown stress conditions, and make differing assumptions of quality and
complexity factors for COTS items. These differing methods yield results that are not
comparable. This specification is intended to provide a standard method for reliability
engineers to perform failure rate predictions for COTS items used in military or high
reliability applications.”
Use Pi Q = 1 (and not 10) for commercial integrated circuits
Use voltage ratio = 0.5 as standard default for semiconductors
“This is considered an average setting for the voltage ratio.”
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 32
Why you cannot predict reliability
Reliability prediction results are frequently unrelated to real-life observations
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 33
Why you cannot predict reliability
Reliability prediction results are frequently unrelated to real-life observations
Applied Reliability Symposium, Europe 2012
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 Feynman
Nobel Prize-winning physicist
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 34
When can reliability prediction be used?
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 35
When can reliability prediction be used?
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 36
When can reliability prediction be used?
Applied Reliability Symposium, Europe 2012
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 used
proactively 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
Track 1
Session 5
Slide Number: 37
When can reliability prediction be used?
Applied Reliability Symposium, Europe 2012
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
Track 1
Session 5
Slide Number: 38
Summary
Applied Reliability Symposium, Europe 2012
The Wonderful One-Hoss-Shay
Oliver Wendell Holmes
Albertyn Barnard, Lambda Consulting
100 years
Track 1
Session 5
Slide Number: 39
Summary
Applied Reliability Symposium, Europe 2012
The Wonderful One-Hoss-Shay
Have you heard of the wonderful one-hoss-shay,
That was built in such a logical way
It ran a hundred years to a day,
And then, of a sudden, it--ah, but stay
I '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
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 40
Summary
Applied Reliability Symposium, Europe 2012
The Wonderful One-Hoss-Shay
"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.
Oliver Wendell Holmes
100 years
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 41
Summary
Applied Reliability Symposium, Europe 2012
Perform reliability prediction based on “published failure data”
Worst method
Prediction based on data unrelated to your product
Exercise in futility
Perform reliability prediction based on “practical prototype test”
Better method
Prediction based on (limited) evidence of actual product reliability
Careful of assumptions and conclusions
“Only game in town”
Perform reliability prediction based on “physics of failure analysis”
Best method
Prediction based on engineering knowledge of failure mechanisms
Technology maturing into practical methods
“Reliability physics”
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 42
Summary
Perform reliability prediction based on “published failure data”
Perform reliability prediction based on “practical prototype test”
Applied Reliability Symposium, Europe 2012
Perform reliability prediction based on “physics of failure analysis”
(Quantification) of reliability is in effect a distraction
to the goals of reliability.
(e-mail from) Ted Kalal
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 43
Where to get more information
Patrick O’Connor and Andre Kleyner,
Practical Reliability Engineering, 5th edition,
John Wiley, 2012
Applied Reliability Symposium, Europe 2012
Accelerated Testing:
www.ReliaSoft.com
www.weibull.com
Physics of Failure:
Center for Advanced Life Cycle Engineering
University of Maryland
www.calce.umd.edu
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 44
Albertyn Barnard
•
•
Applied Reliability Symposium, Europe 2012
•
•
•
•
•
•
•
•
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
ab@lambdaconsulting.co.za
www.lambdaconsulting.co.za
Lambda
Consulting
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 45
Questions
Applied Reliability Symposium, Europe 2012
Thank you for your attention.
Do you have any questions?
Albertyn Barnard, Lambda Consulting
Track 1
Session 5
Slide Number: 46