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Death of a Reliability Engineer (20 years ago)

by Dev Raheja

AND

A Short History of Reliabilityby

James McLinn

AND

The Perils of MTBF by

Fred Schenkelberg

THE R & M ENGINEERING JOURNAL

Published Quarterly (ISSN 0277-9633)

2010 March, Volume 30, Number 1

Reliability Reliability ReviewReview

IDENTIFICATION STATEMENT: Reliability Review (ISSN 0277-9633) is published quarterly (March, June, September, December) for the Reliability Division of the American Society for Qual-ity and mailed to dues paying members. Editorial and Advertising Offi ces 10644 Ginseng Lane, Hanover, Minnesota 55341 Subscription rates for non-members are as follows: Within USA $42.00 yearly; Canada and Mexico, $45.00; Outside North America, $50.00. INDIVIDUAL COPIES: $12.50 per copy plus postage. POSTMASTER: Send address changes to: A S Q, P.O.Box 3005, Milwaukee, WI 53205-3005Domestic Delivery: Non- Profi t Organization and Periodicals Postage Paid at Auburn, CA and additional mailing offi ces; International Delivery: International First Class Mail Post-age Paid at Auburn, CA and additional mailing offi ces.

COPYRIGHT AND REPRINT INFORMATION: Permission of the publisher is required for reprinting or copying for circulation of any article. Write to the publisher, Reliability Review, at the above address. CAVEAT: Comments of individuals do not necessarily have endorsement or constitute offi cial opinion. Copyright ©2010 by the American Society for Quality. Printed in the USA.

ASQ: The Society of Professionals Dedicated to the Advancement of Quality.

Manuscript Submittal Manuscripts: Submit a print out of the complete draft of all text and illustrative material. In addition transmit it as an electronic fi le in MS Word and graphics in Excel or compatible format; (not PDF or Read only) or Word Perfect, or ASCI Text. Email delivery address is [email protected]

Advertising Submit draft copy to James McLinn, Advertising Manager, with Request for Quote. Indicate size desired or sizes of interest. Specify whether you will provide camera-ready copy or desire that we produce fi nal copy.

Letters to the Editor Reliability Review welcomes letters from readers. We offer the following guidelines. Letters should clearly state whether the author is expressing opinion or presenting facts with supporting information. Commendation, encouragement, constructive critique, suggestions and alternative approaches are accepted. Berating is not appropriate. If the content is more than 200 words, we may delete portions to hold to that limit. We reserve the right to edit letters. Address letters to: Trevor Craney, Shell Unconventional Oil, P.O. Box 481, Houston, TX 77001

Authors We encourage R& M professionals to submit original writings discussing concepts, practices and improvement ideas for reliability engineering and management; maintainability, and systems engineering. Our purposes: first, share knowledge, tools and lessons learned; second, to improve design of products and processes; third, to reduce or mitigate risk. Request our template for authors. Authors submitted articles receive staff review to ascertain that facts are correctly stated and opinion and fact are clearly distinguished one from another. Technical content may be refereed by selected specialists. The Editor reserves the right to edit any article, and will usually exercise that right. RR is published on the fi rst of March, June, September and December. If your publishable submittal is received three months before a publication date, your article may be in the next issue - subject to space and reviewing constraints.

Publication StaffChief Editor Editorial Review BoardTrevor Craney X. (Bill) Tian Sam J. Keene, Ph.D. Ward BaunShell, P.O. Box 481 3404 E. Harmony Rd. PO Box 337 UTC PowerHouston, TX 77001-0481 Fort Collins. CO. 80528 Lyons, Colorado 80540 195 Govenors Highway(713) 245-8064 (970) 898-6047 (720) 684-2277 S. Windsor, Conn. [email protected] [email protected] [email protected] (820) 727-7234 [email protected]

Advertising & Publishing Manager Larry George, Ph.D Mingxiao Jiang John Healy, PhD James A. McLinn 1573 Roselli Drive Medtronic Neuromodulation FCC 10644 Ginseng Lane Livermore, CA 94550 7000 Central Ave. NE (215) 847-8094 Hanover, Minnesota 55341 (925) 447-4969 Minneapolis, MN 55432 [email protected](763) 498-8814 [email protected] [email protected]@Aol.com

Reliability ReviewThe R & M Engineering Journal

The Review Presents Volume 30, Number One, March 2010

ITEM SOFTWARE: Advertisement ...................................................i

DIVISION PUBLICATION DETAILS: RELIABILITY REVIEW EDITORS ............................................... 1James McLinn, Publisher The Reliability Division encourages people to submit letters and Trevor Craney, Editor manuscripts to the Reliability Review. In addition we encourage ad- vertisements for classes, tools or services offered for R & M. RELIABILITY CALENDAR OF COMING EVENTS AND NEWS OF THE DIVISION ................................ 3 The Reliability Division supports continued training and information opportunities for all R & M professionals. Also present is Timely News for RD members. . DEATH OF A RELIABILITY ENGINEER .......................................................................................... 4Dev Raheja The reliability profession has chanegd over the last 20 years. ThisReliability Consultant article from 1990 shows how important it is to be proactive in your job. Expectations are to lead teams, drive change and be active. Sitting in the back lab or always at your desk leads to career death.

A SHORT HISTORY OF Reliability ................................................................................................ 7 James McLinn CRE, Fellow In many ways Reliability is still in its infancy. This articles documents the history of reliability as tied to the need for better products. Statistical tools and people are mentioned that impacted the practice of reliability.

RELIASOFT CORPORATION: Lamda Predict Advertisement ......................................... 9

RELEX SOFTWARE CORPORATION: RELIABILITY Advertisement .........................................16, 17

THE PERILS OF MTBF .............................................................................................................. 19 Fred Schenkelberg, CRE MTBF remains the most misused and misunderstood term.. This article demonstrates some of the pitfalls of relying too much on this concept.

RELIABILITY INFORMATION ANALYSIS CENTER: Advertisement ................................................ 24

ADDITIONAL INFORMATION WHAT’S NEW IN RELIABILITY ....................................................26

FULTON FINDINGS: SUPERSMITH WEIBULL ADVERTISEMENT .....................................................26

MANUSCRIPT SUBMITTAL INFORMATION: LOOK HERE BEFORE SUBMITTING .................................27Trevor Craney, Editor PUBLICATIONS ORDER FORM Use our form when ordering RD Publications ............................28

R&M MONOGRAPHS Abstracts of RD Monographs currently available...................30, 31

RELIABILITY DIVISION MANAGEMENT DIRECTORY.......................................................................32

RELIASOFT CORPORATION: Empowerment Ad..............................................33

Page 3 Reliability Review Vol. 30, March 2010

Reliability Engineering Calendar and Training Issues

NEWS OF THE DIVISION

Editorial: 30th Year of Reliability Review

by Trevor A. Craney

A belated Happy New Year to all! This is the thirtieth year of Reliability Review. Most of this successful run has come from the hard work and dedication of Harold Williams, my predecessor. It is not always easy to review papers so critically for the journal, nor is it easy when we have to reject the hard work that an author has put into his or her manuscript. However, we have certainly had some interesting articles over the years. And this issue of Reliability Review is no different.

While Reliability Review has now been around for 30 years, reliability has been around even longer. Jim McLinn’s paper offers his historical perspective of reliability, from its beginnings, through its changed meaning and applications, to where we are today. Fred Schenkelberg has put together his thoughts on his quest to properly educate the world on the consistent misuse and misinterpretation of MTBF. I would venture to say that it’s not a stretch that this metric and its associated simple calculation for reliability using the exponential distribution is perhaps the most associated computation with the term “reliability.” So, again, we have a link to our reliability history.

Finally, to commemorate our 30th year of publication, we are running a special series of articles this year. Each issue of Reliability Review will have a selected reprint of Reliability Review from its past years. These are papers for which we know feedback was considerable, either to the author or the editor. Our fi rst honoree is Dev Raheja, with his 1990 paper, “Death of A Reliability Engineer.” He discusses some problems we had in industry and as a reliability professional… back then. You

Fellow NominationsThe Reliability Division would like

to sponsor any deserving members who meet the criteria for Society Fellow. Go to ASQ.ORG and download the fellow nomination fi le. If you meet the criteria, contact James McLinn, RD Fellow Nominations Committee before April 1 to discuss a possible submission.

[email protected] or 763 498-8814


EVENTS CALENDAR

Dates: Events

See the RD website for a complete list of Training Classes and Conferences

May 24-26, 2010 World Congress on Quality Improvement in St. Louis, MissouriCo-Sponsor: Reliability Division URL: visit ASQ.org and Reliability Div.

NOTE - The new Quality Council of Indiana CRE primer is now available. It covers the contents of the updated CRE exam.

June 15-18, 2010 Applied Reliability Symposium in Reno, NevadaSponsor: ReliaSoftURL: www.ARSymposium.org

Call For papers RAMS 2011 due by April 19, 2010

See RAMS.ORGJan 24-27, 2011 atDisney World, Orlando

continued on page 27

Reliability Review, Vol. 30, March 2010 Page 4 Page 5 Reliability Review Vol. 30, March 2010

Death of a Reliability Engineer by Dev Raheja

chemistry, and fundamental concepts and principles. They combine this knowledge with statistical theory.On the other hand, many so called reliability engineers ignore physics and chemistry, and fail to consider design reliability lessons. They jump straight into statistics. They are lost if hard data are not available. They ignore the fact that reliability can be improved without the statistical analysis. All one has to do is study the failure modes, accelerated testing results and be aware of the customer problems.

The bott om line in reliability is to prevent all failures during the useful life. Some reliability engineers will not agree with this statement. They think a certain level of failures is unavoidable. In my opinion, such engineers should go and perform time and motion studies rather than work as reliability engineers.

A Case HistoryAn engineer was assigned to

work as a Reliability Engineer. He had taken a statistics course in the college he att ended; therefore felt prepared for the position. Soon he began to encounter diffi culty in applying his knowledge of statistics. The test data was never enough; the fi eld data never was complete and was full of errors. He kept complaining about the lack of data collection eff ort. Meanwhile many shoddy products went out the door. No one knew what to do when only a small quantity of data existed.Aft er a few product disappointments, the company decided to go into a full-fl edged reliability program to assure high reliability in the design. The reliability engineer took some practical

When I fi rst wrote the article in March 1990, I implied an ‘F’ grade to the reliability engineers. Now almost 20 years later, I would give them a “E’. Yes there is a litt le improvement but nothing you can write to your mother about. The MTBF cancer was widespread and is still widespread in the DoD. The only reason I upgraded the reliability engineer from F to E is because the MTBF in some industries is no longer used such as in the automotive industry. They use the failure rates instead to hide their shame. Failure rate is just the reciprocal of MTBF. Good job! Same old corn fl akes with a new product name!

Several recent discussions cause me to recall some highlights of a conversation I had over a decade ago with the late Dr. Austin Bonis while we were conducting the fi rst ASQC course on Reliability Engineering. He made the fol¬lowing interesting statement: ‘The design engineer knows a lot but is never able to do a lot; the quality control engineer does not know a lot, therefore does not do a lot; the reliability engineer knows a lot, does a lot, but too late!”

At this time, I am moved to amend his statement so that it reflects a frequent problem, as follows: “The reliability engineer knows a lot, does a lot, but what he does is usually wrong!” Also, “If the basic reliability work is done with a lot of mistakes; then it does not matt er if the work is done too late!”

Some Good, Some BadThere are some excellent reliability

engineers. They have prepared for their tasks with a basic engineering education that included physics,

reliability courses. He then proceeded to apply these skills. The fi rst item on the agenda was reliability prediction. Our reliability engineer was eager to see this task requirement. Now he has a bunch of numbers in his grip. He can compute failure rates for each component from the MIL- HDBK-217E, add them up and calculate the MTBF. The design engineer was not involved. Why involve him? The product is already designed and being tested!

As years pass, the reliability engineer discovers that many gross assumptions were made in his education. The fundamentals of engineering were overlooked. He was taught to assume that the failure rate is constant; even some of the industry “experts” seemed to make that assumption. The real failure rate was rarely constant for any component because of infant mortality failures. Even electronic compo¬nents showed decreasing failure rate with time. For me¬chanical failure mechanisms the failure rates increased with time. The decreasing failure rates would have been good news except that the starting failure rate was anywhere from 10 to 15 percent, which made the customer mad as hell. Eventually, the management got the message that the customer is not supposed to pay for 10 percent defective products and therefore some in-house screening was added. This raised the cost of reliability but the reliability engineer felt more secure. Soon the screening tests became the standard operating procedures.

The work done in computing MTBF still had some value. This was helpful because management was interested in technical merit. Sometimes the customer wanted this information.

Unfortunately, the result of all his work (which cost about four man-months) had very litt le to do with the real MTBF. The failure rates in the MIL-HDBK-217E were outdated, and collected over a large variety of applications. They are based on the assumption of constant failure rate. This implies the failure distribution for components is exponential. Very few real components actually had this failure distribution. The components, even electronics, followed several shapes of failure distributions. All these shapes were ignored. It was too much work to determine the real failure distribution. Such data does not exist in the data banks. Since the whole industry had already been using MIL-HDBK-217E to make reliability predictions, our reliability engineer had no choice but to go along. The predictions, to be credible, should have been a combined result of the review of past experience, qualifi cation tests, and the MIL-HDBK-217E. I suppose a conservative prediction is bett er than no prediction. Such predictions can always be adjusted by multiplying with so called experience factors, sometimes crudely called the fudge factors!

A r r h e n i u s M o d e lOur reliability engineer was told

that the Arrhenius model applies to electronic components. He was not sure but he did not question the judgment of those with over 20 years experience. But he did fi nd out later that many failures in electronics are mechanical. The Arrhenius distribution did not apply to such failures. But he found it convenient to use Arrhenius as long as everyone around is a believer in it. He also used the Activation Energy

Death of a Reliability Engineer (cont.)

Reliability Review, Vol. 30, March 2010 Page 6 Page 7 Reliability Review Vol. 30, March 2010

Death of a Reliability Engineer (cont.)constant from the published data of device manufacturers. This never made sense to him since his devices were not built exactly the same as the original manufacturer built them; but he had to go along with it. This company never had money to run a few experiments to assess the real Activation Energy constant. The management had a great TQM (Total Quality Management) program. But that was only in name, not in spirit. When the time came to put money on the table for quality improvements, the management was very unhappy. The TQM program meant that you talk up improved quality but do not spend time or money implementing new eff ort.

FMEA and Fault TreesAft er becoming frustrated with

the make believe world of reliability numbers (my opinion), the reliability engineer sought more tools. He found the Failure Mode Eff ects, and Criticality Analysis (FMECA) and the Fault Tree Analysis (FTA). But, he did not quite know how to use them. The experts confused him more than they helped. Every expert had his own way and industry was already misusing these tools. Many were using these tools to perform reliability estimates and modeling rather than improving the product design. Reliability engineering labored many months to perform these analyses. They helped get attention on the failure rates but did not make much impact on the design engineer. Management was satisfi ed because these are the tools everyone is supposed to use.

The MIL-HDBK-217E predictions, the FMECA, and the Fault Trees were impressive. They dazzled

management. That is, until the recession hit the industry. Then Management began searching for places to cut costs; non-essential tasks became a target. They found reliability engineering to be a non-essential cost. Then, the reliability engineer was given the pink slip and the whole reliability engineering department was eliminated to achieve profi tability.

What Went WrongThere is a long list of things that

went wrong. I will mention only a few. The tools such as FMECA and the Fault Trees were used by the reliability engineer. He is not qualifi ed to use them because he does not know all the details of the design. He should always use them together with the design engineer. They should work as a team - BEFORE the design is released, not aft er. The tools should have been used for design improvement. The MIL-HDBK-217E should be used for comparing design options, not for fi eld reliability prediction. There are too many assumptions in the MIL-HDBK-217E which the user is not aware of. The errors may not aff ect the comparison between two design options. The screening tests were used for inspecting the product rather than learning to eliminate the failure modes and lower the production costs. These observations show that the reliability engineer requested design changes that increased, rather than reduced, costs.

Look Into The MirrorThe above example is not uncommon.

Look at yourself in the mirror. Possibly you will find similarities in your situation. I fi nd indications worldwide that when misusing the tools continues,


A Short History of Reliability by James McLinn CRE

Reliability is a popular concept that has been celebrated for years as a commendable att ribute of a person or a product. Its modest beginning was in 1816, far sooner than most would guess. The word “reliability” was fi rst coined by poet Samuel Taylor Coleridge [17]. In statistics, reliability is the consistency of a set of measurements or measuring instrument, oft en used to describe a test. Reliability is thought to be inversely related to random error [18]. In Psychology, reliability refers to the “consistency of a measure”. A test is considered reliable if we get the same result repeatedly. For example, if a test is designed to measure a trait (such as introversion), then each time the test is administered to a subject, the results should be approximately the same [19]. Thus, before World War II, reliability as a word came to mean “dependability” or “repeatability”. The modern use was re-defi ned by the U.S. military in the 1940s and evolved slowly to the presentmeaning. The term initially came to mean that a product that would “operate when expected”. The current meaning connotes a number of additional att ributes that now span products, service applications, soft ware packages or human activity. These att ributes now pervade every aspect of our present day technologically-intensive world. Let’s follow the journey of the word “reliability” from the early days to present. People, places and events will be noted along this journey.

An early application of reliability might relate to the telegraph. That was a batt ery powered system with simple transmitters and receivers connected by wire. The main failure mode might have been a broken

wire or insuffi cient voltage. Until the light bulb, the telephone, and AC power generation and distribution, there was not much new in electronic applications for reliability. Before 1915, Frederick W. Taylor had worked on ways to make products more consistent and the manufacturing process more effi cient. He was the fi rst to separate the engineering from management and control [21]. By 1916, radios with a few vacuum tubes began to appear in the public. Automobiles came into more common use by 1920 and may represent mechanical applications of reliability. In the 1920s, product improvement through the use of statistical quality control was promoted by Dr. Walter A. Shewhart at Bell Labs [20]. On a parallel path with product reliability was the development of statistics in the twentieth century. Statistics as a tool for making measurements would become inseparable with the development of reliability concepts.

At this point, designers were still responsible for product quality and reliability and repair people took care of the failures. There wasn’t much planned proactive prevention or economic justification for doing so. Charles Lindberg required that the 9 cylinder air cooled engine for his 1927 transatlantic fl ight be capable of 40 continuous hours of operation without maintenance [22]. Much individual progress was made into the 1930s in a few specifi c industries. Quality and process measures were in their infancy, but growing. Wallodie Weibull was working in Sweden during this period and investigated the fatigue of materials. He created a distribution, which we now call Weibull, during this time [1]. In the 1930s, Rosen and

Reliability Review Vol. 30, March 2010 Page 8

Rammler were also investigating a similar distribution to describe the fi neness of powdered coal [16].

By the 1940s, reliability and reliability engineering still did not exist. The demands of WWII introduced many new electronics products into the military. These ranged from electronic switches, vacuum tube portable radios, radar and electronic detonators. Electronic tube computers were started near the end of the war, but did not come into completion until aft er the war. At the onset of the war, it was discovered that over 50% of the airborne electronics equipment in storage was unable to meet the requirements of the Aircorp. and Navy [1, page 3]. More importantly, much of reliability work of this period also had to do with testing new materials and fatigue of materials. M.A. Miner published the seminal paper titled “Cumulative Damage in Fatigue” in 1945 in an ASME Journal. B. Epstein published “Statistical Aspects of Fracture Problems” in the Journal of Applied Physics in February 1948 [2]. The main military application for reliability was still the vacuum tube, whether it was in radar systems or other electronics. These systems had proved problematic and costly during the war. For shipboard equipment aft er the war, it was estimated that half of the electronic equipment was down at any given time [3]. Vacuum tubes in sockets were a natural cause of system intermitt ent problems. Banging the system or removing the tubes and re-installing were the two main ways to fi x a failed electronic system. This process was gradually giving way to cost considerations for the military. They couldn’t aff ord to have half of their essential equipment non-functional all of the time. The operational and logistics

A Short History of Reliability (cont.) costs would become astronomical if this situation wasn’t soon rectifi ed. IEEE formed the Reliability Society in 1948 with Richard Rollman as the first president. Also in 1948, Z.W. Birnbaum had founded the Laboratory of Statistical Research at the University of Washington which, through its long association with the Offi ce of Naval Research, served to strengthen and expand the use of statistics [23].

The start of the 1950s found the bigger reliability problem was being defi ned and solutions proposed both in the military and commercial applications. The early large Sperry vacuum tube computers were reported to fi ll a large room, consume kilowatt s of power, have a 1024 bit memory and fail on the average of about every hour [8]. The Sperry solution was to permit the failed section of the computer to shut off and tubes replaced on the fl y. In 1951, Rome Air Development Center (RADC) was established in Rome, New York to study reliability issues with the Air Force [24]. That same year, Wallodi Weibull published his fi rst paper for the ASME Journal of Applied Mechanics in English. It was titled “A Statistical Distribution Function of Wide Applicability” [28]. By 1959, he had produced “Statistical Evaluation of Data from Fatigue and Creep Rupture Tests: Fundamental Concepts and General Methods” as a Wright Air Development Center Report 59-400 for the US military.

On the military side, a 1950 study group was initiated. This group was called the Advisory Group on the Reliability of Electronic Equipment, AGREE for short [4, 5]. By 1952, an initial report by this group recommended the following three items for the creation of reliable systems:

Reliability Review Vol. 30, March 2010 Page 10 Page 11 Reliability Review Vol. 30, March 2010

A Short History of Reliability (cont.) A Short History of Reliability (cont.)statistical confi dence for products. Also recommended was running longer and harsher environmental tests that included temperature extremes and vibration. This came to be known as AGREE testing and eventually turned into Military Standard 781. The last item provided by the AGREE report was the classic defi nition of reliability. The report stated that the defi nition is “the probability of a product performing without failure a specifi ed function under given conditions for a specifi ed period of time”. Another major report on “Predicting Reliability” in 1957 was that by Robert Lusser of Redstone Arsenal, where he pointed out that 60% of the failures of one Army missile system were due to components [7]. He showed that the current methods for obtaining quality and reliability for electronic components were inadequate and that something more was needed. ARINC set up an improvement process with vacuum tube suppliers and reduced infant mortality removals by a factor of four [25]. This decade ended with RCA publishing information in TR1100 on the failure rates of some military components. RADC picked this up and it became the basis for Military Handbook 217. This decade ended with a lot of promise and activity. Papers were being published at conferences showing the growth of this fi eld. Consider the following examples: “Reliability Handbook for Design Engineers” published in Electronic Engineers, number 77, pp. 508-512 in June 1958 by F.E. Dreste and “A Systems Approach to Electronic Reliability” by W.F. Leubbert in the Proceedings of the I.R.E., vol. 44, p. 523 in April, 1956 [7]. Over the next several decades, Birnbaum made signifi cant contributions to probabilistic inequalities (i.e. Chebychev), non-parametric statistics, reliability of complex

systems, cumulative damage models, competing risk, survival distributions and mortality rates [23]. The decade ended with C.M. Ryerson producing a history of reliability to 1959 [26] published in the proceedings of the IRE.

The 1960s dawned with several signifi cant events. RADC began the Physics of Failure in Electronics Conference sponsored by Illinois Institute of Technology (IIT). A strong commitment to space exploration would turn into NASA, a driving force for improved reliability of components and systems. Richard Nelson of RADC produced the document “Quality and Reliability Assurance Procedures for Monolithic Microcircuits,” which eventually became Mil-Std 883 and Mil-M 38510. Semiconductors came into more common use as small portable transistor radios appeared. Next, the alternator became possible with low cost germanium and later silicon diodes able to meet the under-the-hood requirements. Dr. Frank M Gryna published a Reliability Training Text through the Institute of Radio Engineers. The nuclear power industry was growing by leaps and bounds at that point in history. The demands of the military ranging from missiles to airplanes, helicopters and submarine applications drove a variety of technologies. The study of the eff ects of EMC on systems was initiated at RADC and this produced many developments in the 1960s.

During this decade, a number of people began to use, and contribute to the growth and development of, the Weibull function, the common use of the Weibull graph, and the propagation of Weibull analysis methods and applications. A few of these people

1 ) T h e r e wa s a n e e d t o develop better components and more consistency from suppliers.

2) The military should establish quality and reliability requirements f o r c o m p o n e n t s u p p l i e r s .

3) Actual field data should be collected on components in order to establish the root causes of problems.

In 1955, a conference on electrical contacts and connectors was started, emphasizing reliability physics and understanding failure mechanisms. Other conferences began in the 1950s to focus on some of these important reliability topics. That same year, RADC issued “Reliability Factors for Ground Electronic Equipment.” This was authored by Joseph Naresky. By 1956, ASQC was off ering papers on reliability as part of their American Quality Congress. The radio engineers, ASME, ASTM and the Journal of Applied Statistics were contributing research papers. The IRE was already holding a conference and publishing proceedings titled “Transaction on Reliability and Quality Control in Electronics”. This began in 1954 and continued until this conference merged with an IEEE Reliability conference and became the Reliability and Maintainability Symposium

In 1957, a fi nal report was generated by the AGREE committ ee and it suggested the following [6]. Most vacuum tube radio systems followed a bathtub-type curve. It was easy to develop replaceable electronic modules, later called Standard Electronic Modules (or SEMs), to quickly restore a failed system and they emphasized modularity of design. Additional recommendations included running formal demonstration tests with

who helped develop Weibull are mentioned here. First, Dorian Shainin wrote an early booklet on Weibull in the late 1950s, while Leonard Johnson at General Motors helped improve the plott ing methods by suggesting median ranks and beta Binomial confi dence bounds in 1964. Professor Gumbel demonstrated that the Weibull distribution is a Type III Smallest Extreme Value distribution [9]. This is the distribution that describes a weakest link situation. Dr. Robert Abernethy was an early adaptor at Pratt and Whitney, and he developed a number of applications, analysis methods and corrections for the Weibull function.

In 1963, Weibull was a visiting professor at Columbia and there worked with professors Gumbel and Freudenthal in the Institute for the Study of Fatigue and Reliability. While he was a consultant for the US Air Force Materials Laboratory, he published a book on materials and fatigue testing in 1961. He later went on to work for the US military, producing reports through 1970 [9].

A few additional key dates and events in this decade should be mentioned. In 1962, G.A. Dodson and B.T. Howard of Bell Labs published “High Stress Aging to Failure of Semiconductor Devices” in the Proceedings of the 7th National Symposium of Reliability and Quality Control [5]. This paper justifi ed the Arrhenius model for semiconductors. Lots of other papers at this conference looked at other components for improvement. By 1967, this conference was re-titled as the Reliability Physics Symposium, RPS, with “International” being added a few years later. Really, 1962 was a key year with the fi rst issue of Military Handbook 217 by the Navy. Already, the two main branches


A Short History of Reliability (cont.)This was important for new models that described degradation processes.

During the decade of the 1970s, work progressed across a variety of fronts. In this decade, the use and variety of ICs increased. Bipolar, NMOS and CMOS all developed at an amazing rate. In the middle of the decade, ESD and EOS were covered by several papers and eventually evolved into a conference by the decade end. Likewise, passive components which were once covered by IRPS, moved to a Capacitor and Resistor Technology Symposium (CARTS) for continued advancement on all discrete components. A few highlights of the decade were the fi rst papers on gold aluminum intermetalics, accelerated testing, the use of Scanning Electron Microscopes for analysis and loose particle detection testing (PIND). In mid-decade, Hakim and Reich published a detailed paper on the evaluation of plastic encapsulated transistors and ICs based upon fi eld data. Other areas being studied included gold embritt lement, PROM nichrome link grow back, moisture out gassing of glass sealed packages and problems with circuit boards. Perhaps the two most memorable reliability papers from this decade were one on soft error rates caused by alpha particles (Woods and May) and on accelerated testing of ICs with activation energies calculated for a variety of failure mechanisms by D.S. Peck. By the end of the decade, commercial fi eld data were being collected by Bellcore as they strived to achieve no more than 2 hours of downtime over 40 years. This data became the basis of the Bellcore reliability prediction methodology [28].

The Navy Material Command

brought in Willis Willoughby from NASA to help improve military reliability across a variety of platforms. During the Apollo space program, Willoughby had been responsible for making sure that the spacecraft worked reliably all the way to the moon and back. In coming to the Navy, he was determined to prevent unreliability. He insisted that all contracts contain specifi cations for reliability and maintainability instead of just performance requirements. Willoughby’s eff orts were successful because he attacked the basics and worked upon a broad front. Wayne Tustin credits Willoughby with emphasizing temperature cycling and random vibration, which became ESS testing. This was eventually issued as a Navy document P-9492 in 1979. Next, he published a book on Random Vibration with Tustin in 1984. Aft er that, he replaced older quality procedures with the Navy Best Manufacturing Practice program. The microcomputer had been invented and was making changes to electronics while RAM memory size was growing at a rapid rate. Electronic calculators had shrunk in size and cost and now rivaled early vacuum tube computers in capability by 1980. Military Standard 1629 on FMEA was issued in 1974, and human factors engineering and human performance reliability had been recognized by the Navy as important to the operating reliability of complex systems and work continued in this area. They led groundbreaking work with a Human Reliability Prediction System User’s Manual in 1977 [12]. The Air Force contributed with the Askren-Regulinski exponential models for human reliability. NASA made great strides at designing and developing spacecraft such as the space shutt le. Their emphasis was on risk

of reliability existed. One branch existed for investigation of failures and the other for predictions. Later in the decade was the fi rst paper on step stress testing by Shurtleff and Workman that set limits to this technique when applied to Integrated Circuits. J.R. Black published his work on the physics of electromigration in 1967. The decade ended studies of wafer yields as silicon began to dominate reliability activities and a variety of industries. By this time, the ER and TX families of specifi cations had been defined. The U.S. Army Material Command issued a Reliability Handbook (AMCP 702-3) in October of 1968, while Shooman’s Probabilistic Reliability was published by McGraw-Hill the same year to cover statistical approaches. The Automotive industry was not to be outdone and issued a simple FMEA handbook for improvement of suppliers. This was based upon work done on failure mode investigation and root cause by the military, but not yet published as a Military standard. Communications were enhanced by the launch of a series of commercial satellites, INTELSAT. These provided voice communications between the U.S. and Europe. Around the world, in other countries, professionals were beginning to investigate reliability and participate with papers at conferences. The decade ended with a landing on the moon showing how far reliability had progressed in only 10 years. Human reliability had now been recognized and studied, which resulted in a paper by Swain on the techniques for human error rate prediction (THERP) [11]. In 1969, Birnbaum and Saunders described a life distribution model that could be derived from a physical fatigue process where crack growth causes failure [23].

A Short History of Reliability (cont.)management through the use of statistics, reliability, maintainability, system safety, quality assurance, human factors and software assurance [10]. Reliability had expanded into a number of new areas as technology rapidly advanced.

The 1980s was a decade of great changes. Televisions had become all semiconductor. Automobiles rapidly increased their use of semiconductors with a variety of microcomputers under the hood and in the dash. Large air conditioning systems developed electronic controllers, as had microwave ovens and a variety of other appliances. Communications systems began to adopt electronics to replace older mechanical switching systems. Bellcore issued the first consumer prediction methodology for telecommunications and SAE developed a similar document SAE870050 for automotive applications [29]. The nature of predictions evolved during the decade and it became apparent that die complexity wasn’t the only factor that determined failure rates. Kam Wong published a paper at RAMS questioning the bathtub curve [25]. During this decade, the failure rate of many components dropped by a factor of 10. Soft ware became important to the reliability of systems; this discipline rapidly advanced with work at RADC and the 1984 article “History of Software Reliability” by Martin Shooman [13] and the book Soft ware Reliability – Measurement, Prediction, Application by Musa et.al. Complex soft ware-controlled repairable systems began to use availability as a measure of success. Repairs on the fl y or quick repairs to keep a system operating would be acceptable. Soft ware reliability developed models such as Musa Basic to predict the number of missed soft ware faults that might remain in code. The Naval


A Short History of Reliability (cont.)A Short History of Reliability (cont.)


Surface Warfare Center issued Statistical Modeling and Estimation of Reliability Functions for Soft ware (S.M.E.R.F.S) in 1983 for evaluating soft ware reliability. Developments in statistics made an impact on reliability. Contributions by William Meeker, Gerald Hahn, Richard Barlow and Frank Proschan developed models for wear, degradation and system reliability. Events of note in the decade were the growing dominance of the CMOS process across most digital IC functions. Bipolar technologies, PMOS and NMOS gave way in most applications by the end of the decade. CMOS had a number of advantages such as low power and reliability. High speed applications and high power were still dominated by Bipolar. At the University of Arizona, under Dr. Dimitri Kececioglu, the Reliability Program turned out a number of people who later became key players in a variety of industries. The PC came into dominance as a tool for measurement and control. This enhanced the possibility of canned programs for evaluating reliability. Thus, by decade end, programs could be purchased for performing FMEAs, FTAs, reliability predictions, block diagrams and Weibull Analysis. The Challenger disaster caused people to stop and re-evaluate how they estimate risk. This single event spawned a reassessment of probabilistic methods. Pacemakers and implantable infusion devices became common and biomedical companies were quick to adopt the high reliability processes that had been developed by the military. The Air Force issued the R&M 2000 which was aimed at making R&M tasks normal business practice [26]. David Taylor Research Center in Carderock Maryland commissioned a handbook of reliability prediction

procedures for mechanical equipment to Eagle Technology in 1988 [30]. This was typically called the Carderock Handbook and was issued by the Navy in 1992 as NSWC 92/L01[31]. Altogether, the1980s demonstrated progress in reliability across a number of fronts from military to automotive and telecommunications to biomedical. RADC published their fi rst Reliability Tool Kit and later updated this in the 1990s for COTS applications. The great quality improvement driven by competition from the Far East had resulted in much better components by decade end.

By the 1990s, the pace of IC development was picking up. New companies built more specialized circuits and Gallium Arsenide emerged as a rival to silicon in some applications. Wider use of stand alone microcomputers was common and the PC market helped keep IC densities following Moore’s Law and doubling about every 18 months. It quickly became clear that high volume commercial components oft en exceeded the quality and reliability of the small batch specially screened military versions. Early in the decade, the move toward Commercial Off the Shelf (COTS) components gained momentum. With the end of the cold war, the military reliability changed quickly. Military Handbook 217 ended in 1991 at revision F2. New research developed failure rate models based upon intrinsic defects that replaced some of the complexity-driven failure rates that dominated from the 1960s through the 1980s. This eff ort was led by RAC (the new name for RADC) and resulted in PRISM, a new approach to predictions. Reliability growth was recognized for components in this document. Many of the military specifications became obsolete and best commercial practices were oft en

adopted. The rise of the internet created a variety of new challenges for reliability. Network availability goals became “fi ve 9s or 5 minutes annually” to describe the expected performance in telecommunications. The decade demanded new approaches and two were initiated by the military. Sematech issued Guidelines for Equipment Reliability in 1992 [34]. The SAE issued a handbook on the reliability of manufacturing equipment in 1993 [32]. This was followed in 1994 by the SAE G-11 committ ee issuing a reliability journal [35]. Richard Sadlon, at RAC, produced a mechanical application handbook the same year [33]. The Army started the Electronic Equipment Physics of Failure Project and engaged the University of Maryland CALCE center, under Dr. Michael Pecht, as part of the process. The Air Force initiated a tri-service program that was cancelled later in the decade. On the soft ware side, the Capability Maturity Model (CMM) was generated. Companies at the highest levels of the model were thought to have the fewest residual faults [14]. RAC issued a six set Blueprint for Establishing Eff ective Reliability Programs in 1996 [35]. The internet showed that one single soft ware model would not work across the wide range of worldwide applications, which now includes wireless. New approaches were required such as soft ware mirroring, rolling upgrades, hot swapping, self-healing and architecture changes [15]. New reliability training opportunities and books became available to the practitioners. ASQ made a major update to its professional certifi cation exam to keep pace with the changes evident. ISO 9000 added reliability measures as part of the design and

development portion of the certifi cation. The turn of the century brought

Y2K issues for soft ware. The expansion of the world-wide-web created new challenges of security and trust. Web-based information systems became common, but were found not to be secure from hacking. Thus, the expanded use of the web to store and move information could be problematic. The older problem of too little reliability information available had now been replaced by too much information of questionable value. Consumer reliability problems could now have data and be discussed online in real time. Discussion boards became the way to get questions answered or fi nd resources. Training began the move toward webinars, rather than face-to-face classes. Insurance, banking, job hunting, newspapers, music, baseball games and magazines all went online and could be monitored in real time or downloaded. New technologies such as micro-electro mechanical systems (MEMS), hand-held GPS, and hand-held devices that combined cell phones and computers all represent challenges to maintain reliability. Product development time continued to shorten through this decade and what had been done in three years was now done in 18 months. This meant reliability tools and tasks must be more closely tied to the development process itself. Consumers have become more aware of reliability failures and the cost to them. One cell phone developed a bad reputation (and production soon ceased) when it logged a 14% fi rst year failure rate. In many ways, reliability became part of everyday life and consumer expectations.



Every organization talks about product reliability in some manner. Sometimes our customers provide explicit reliability requirements. Sometimes our customers have an expected metric to report reliability expectations. Our industry may have a “standard” means to discuss reliability. Or, we may have a local “tradition”.

One of the most common is MTBF

MTBF, or Mean Time Between Failure, and the many variations of this term have one thing in common. It is the most misunderstood four lett er acronym in engineering. For the purpose of this discussion I am using MTBF and most of the comments equally apply to MTTF, MTBUR, etc., which are acronyms for Mean Time To Failure and Mean Time Between Unscheduled Removals.

During a presentation (Schenkelberg 2007) on this subject to a group of reliability professionals, I asked if anyone in the room had encountered trouble with MTBF. Nearly every person of the over 100 in att endance quickly raised their hand. We spent the next hour sharing horror stories resulting from the misuse of MTBF. We traded approaches to educate engineers, managers, customers, and vendors on the actual meaning and proper use of MTBF, plus when to use other measures.

What is MTBF?Technically, MTBF (MTTF actually,

more on that later) is commonly assumed to be the unbiased estimator of the exponential distribution parameter, theta (1995). Actually t is the expected value

or mean of the life data distribution. This is based on how we calculate the value based on either test or fi eld data. For example, if we have 50 units that all run for 100 hours and right at the end of 100 hours one of the units fails. We can calculate the MTBF as follows. First determine the total hours all the units operated. That’s easy, 50 units times 100 hours is 5,000 hours. Then divide the total operating hours by the number of failures. In this simple example, that is one, for a resulting:

MTBF = 5,000/1 = 5,000 hourswith

Eexp(t) = ∫(t)λe-λt dt = 1/λ = θ

MTBF = θ = 1/λ

θ = ∫o∞(t)f(t)dt

θ = ∫o∞(t) λe-λt dt

Figure 1. Estimation of MTBF or Theta for Exponential Distribution

It is the inverse of the failure rate that permits a simple estimate of the distribution parameter. Using this, note that if we had 100 units run for 50 hours and had one failure at the end of 50 hours, the result is the same as either of the two following scenarios: One unit runs for 5,000 hours before failing or 5,000 units each run for one hour, then one fails. Well, it is not so strange if the underlying failure mechanism has an equal chance of causing a failure every hour (or moment). If the chance of failure is constant, or we say the hazard

A Short History of Reliability (cont.) The Perils of MTBF by Fred SchenkelbergFootnotes

1. Thomas, Marlin U., Reliability and Warranties: Methods for Product Development and Quality Improvement, CRC, New York, 20062. Kapur, K.C. and Lamberson, L.R., Reliability in Engineering Design, Wiley, 1977, New York3. Ralph Evans, “Electronics Reliability: A personal View”, IEEE Transactions on Reliability, vol 47, no. 3 September 1998, pp. 329-332, 50 th Anniversary special edition.4. O’Connor, P.D.T., Practical Reliability Engineering, Wiley, 4th edition, 2002, New York, pp. 11-135. George Ebel “Reliability Physics in Electronics: A Historical View”, IEEE Transactions on Reliability, vol 47, no. 3 September 1998, pp. 379-389, 50 th Anniversary special edition6. Reliability of Military Electronic Equipment, Report by the Advisory Group on Reliability of Electronic Equipment, Offi ce of the Assistant Secretary of Defense (R&D), June 1957 7. Lloyd, David, and Lipow, Myron, Reliability: Management, Methods and Mathematics, Prentice Hall, 1962, Englewood Cliff s8. Personal report of Gus Huneke, Failure Analysis Manager who I worked with at Control Data Corporation in the late 1970s. Gus had worked on these early computer systems as a young engineer in the early 1950s at Univac.9. Abernethy, Robert, The New Weibull Handbook, 4 th edition, self published, 2002, ISBN 0-9653062-1-6 10. Vincent Lalli, “Space-System Reliability: A Historical Perspective”, IEEE Transactions on Reliability, vol 47, no. 3 September 1998, pp. 355-360, 50 th Anniversary special edition11. Swain, A.D., T.H.E.R.P. (Techniques for Human Error Rate Prediction), SC-R-64-1338, 1964 by Sandia National Labs.12. Siegel, A.I., LaSala, K.P. and Sontz, C., Human Reliability Prediction System User’s Manual, Naval Sea Systems Command, 1977

13. M. Shooman, “Soft ware Reliability: A Historical Perspective”, IEEE Transactions on Reliability, vol R-33, 1984, pp.48-5514. S. Keene, “Modeling Software R&M Characteristics” Reliability Review, Vol 17 No 2 & 3, 1997.15. Henry Malec, “Communications Reliability: A Historical Perspective”, IEEE Transactions on Reliability, vol 47, no. 3 September 1998, pp. 333-344, 50 th Anniversary special edition16. Dodson, Bryan, The Weibull Analysis Handbook, second edition, ASQ, Milwaukee, 200617. Saleh, J.H. and Marais, Ken, “Highlights from the Early (and pre-) History of Reliability Engineering”, Reliability Engineering and System Safety, Volume 91, Issue 2, February 2006, Pages 249-25618. htt p://en.wikipedia.org/wiki/Reliability, (statistics defi nition)19. http://psychology.about.com/od/researchmethods/f/reliabilitydef.htm - Reliability, (Psychology defi nition)20. Juran, Joseph and Gryna, Frank, Quality Control Handbook, Fourth Edition, McGraw-Hill, New York, 1988, p.24.3 21. Juran, Joseph editor, A History of Managing for Quality, ASQC Press, Milwaukee 1995, pp. 555-55622. htt p://en.wikipedia.org/wiki/Spirit_of_St._Louis23. Birnbaum, W.Z., Obituary, Department of Mathematics, Dec 15, 2000 at University of Washington, htt p://www.math.washington.edu/~sheetz/Obituaries/zwbirnbaum.html24. Reliability Analysis Center Journal, 1Q, 1998 from RAC25. Kam Wong, “Unifi ed Field (Failure) Theory-Demise of the Bathtub Curve”, Proceedings of Annual RAMS, 1981, pp402-40826. Raymond Knight, “Four Decades of Reliability Progress”, Proceedings of Annual RAMS, 1991, pp156-16027. William Denson, “The History of Reliability Predictions”, IEEE Transactions on Reliability, vol. 47, no. 3 September 1998, pp. 321-328, 50 th Anniversary special edition.


hour of the experiment with 1000 light bulbs, 10 bulbs should have failed (1000 x 1/100 = 10 failures in one hour). When there are only 500 light bulbs remaining, it takes two hours to incur 10 failures (500 x 1/100 = 5 failures in one hour).

Figure 4. Only 368 of original 1000 still operating at 100 hours

Another way to determine the answer to how many will still be working at the end of 100 hours is to use the exponential distribution’s reliability function. Figure 5 shows the function and associated calculation. We would expect 36.8 light bulbs to still be operating aft er 100 att empt to operate for 100 hours.

R(t) = e -(t/θ)

Figure 5. Calculation for Reliability at 100 hours

T - Stands for TimeHours, cycles, years, pages and many

more ways of counting some form of use are common. Keep in mind that the MTBF is the inverse of failure rate

The Perils of MTBF (cont.) distribution in my home, it is close enough. As such there is no rationale to conduct preventative maintenance. The memoryless feature of the distribution suggests the new bulb has exactly the same chance of failure in the next hour as the existing working light bulb. So there is no time or cost benefi t to the preventative replacement.

Now, if your community is like mine, you receive annual reminders to change the batt eries in your smoke detectors. Those 9V batt eries do tend to drain. Each batt ery drains or is consumed (“wears”) at slightly different rates, due to variation in initial power density, contact resistance, power demands, etc. The batt eries last a bit longer than a year and do not follow a constant failure rate at all. Aft er about a year, the smoke detectors oft en begin to trigger low power alarms.

This then leads to the annual eff ort to change all of the 9V batt eries in smoke detectors. I’ve seen the same behavior in offi ce buildings using fl uorescent tube lighting. The maintenance crews tend to replace entire banks of tubes. When queried, I learned of their experience. “When one goes, then all will fail soon aft er. So, while we have the ladder out, we just replace them all.”

There are a few common “issues” with MTBF. In support of the phrase “worst four lett er acronym”, consider each element of the four letters.

M - Stands for Mean

Speaking statistically, this is the expected value or the fi rst moment of the distribution. Each distribution has a mean value. The general formula for the expected value (1995), denoted E(x), is shown in Figure 3.

rate or failure rate is constant, then the above method to estimate MTBF is valid.

Eweib2p(t) = ηΓ(1 + 1/β)

Elogn(t) = eμ+(1/2)σ2

Figure 2. Expected Values for Weibull and Lognormal Distributions

There are bett er ways to estimate MTBF when the assumption of a constant failure rate is not true. When the failure rate is changing over time, as with bearing wear out, the exponential distribution is a very poor means to describe the behavior. It is similar to describing a parabola with a straight line. The straight line just doesn’t have enough information to describe the curve. Yet, most oft en MTBF is calculated as described above using the constant failure rate or exponential distribution assumption. Figure 2 shows two other life data distribution expected values. After fitting the data to the appropriate distribution, formulas like these also provide the MTBF value, and without making the constant failure rate assumption.

Light Bulbs & Smoke Detectors

How often do you change incandescent light bulbs? Randomly, right? When a bulb burns out, you find a spare bulb and replace the burned out one. Do you then think about changing the rest of the similar light bulbs in the house? Probably not.

Incandescent light bulbs tend to follow the exponential life distribution. This is not exactly true (Donald L Klipstein 2006), yet in my experience and limited data of the time-to-failure

The Perils of MTBF (cont.) E(x) = ∫-∞

∞(x)f(x)dx

Figure 3. General Expected Value from probability density function

The issue stems, in my opinion, from those undergraduate statistics classes most would rather forget. The normal (Gaussian) distribution dominated those lectures. Many sections and test questions started with the phrase, “Assuming a normal distribution....” It was drilled into our engineering minds. The learned response was that “mean” is “average”, as well as the 50th percentile of a normal distribution. One half of values are above and one half are below.

Therein dwells the root of a mistaken understanding of MTBF. Not all distributions have the same properties concerning mean values, which was most likely not mentioned during the undergraduate statistics course. For example, the exponential family of distributions has an expected value, or mean, which is defi ned as the 63.2 percentile. About one third (36.79%) of values are above the mean and about two thirds (63.21%) are below the mean.

Let’s assume we have 1000 light bulbs with an MTBF of 100 hours. How many will still be working at the end of 100 hours of operation? To answer this question, consider that for each hour, each light bulb has a 1 in 100 chance of failing. Therefore, we expect to lose about 10 in the fi rst hour. Surprised? This is as expected if using the reliability function of the exponential distribution.

If we run the time out a litt le further, the plot shows what we commonly call the exponential decay. The chance of failure each hour for each light bulb is the same. It just takes more time to have the same number of failures. In the fi rst



The Perils of MTBF (cont.)The Perils of MTBF (cont.)in the exponential model. The failure rate units are the number of failures per unit time. Inverting this gives us units of time (hours, cycles, years, ...) per failure.

I am not sure why (tend to think it was a marketing decision) someone decided to invert the negative connotation of “failures/hour” into the positive sounding “hours/failure”. Therein clicks another issue with MTBF. The units of MTBF, frequently in operating hours, is oft en confused with clock or calendar time. It really is a confusing unit of measure to convey the probability of failure. Instead of stating a light bulb has a 0.01 chance of failure per hour of operation, our dislike for numbers between 0 and 1 (recall probability and stats classes!) is avoided by inverting the failure rate. Now it reads 100 hours MTBF. This apparently sounds much bett er.

B - Stands for Between (or Before?)

Either way, between or before, when linked with the rest of the acronym it conveys a failure free period. It would have been bett er to state MTF, Mean Time of Failures. While that suggestion isn’t really that good, the idea of a failure free period, is not part of the defi nition. I heard one design team manager explain MTBF as the time to expect from one failure to the next. This is time between failures. So, once a failure occurs, we have the MTBF hours before we would expect the next failure.

MTTF, the closely associated metric to MTBF, uses “To” instead of “Between”, and creates the same confusion. With “To”, “Before” or “Between”, about two thirds (63.2%) of the light bulbs will fail at the 100 hour mark. And, they will fail randomly

across the entire duration of interest.When the MTBF value is very

large, say 1 million hours, it may seem like a failure free period is occurring. It’s just that the probability of failure is very small, 1 in a million chance of failure per hour. Running a test of 10 light bulbs for 1000 hours with an actual 1 million hour MTBF, then the probability of failure would result in an expected ZERO failures (actually this number is an expected 1% units failing - it may take an average of ten runs of the test for a single bulb to fail), as shown in the calculation in Figure 6.

R(t) = 1-F(t)

F(10 x 1000) = 1 - e-(10,000/1,000,000)

F(10,000) = 1 - 0.99 = 0.01 Figure 6. Probability of failure is prett y low for very high MTBF values

F - Stands for Failure

Who defines “failure” in your organization? If your customers return a product, are they classifi ed “No Trouble Found”? In a classical sense, a product failure is when the product does not meet stated performance specifi cations. Yet, customers will return products that fail to meet their expectations, as opposed to performance specifi cations, and it still creates warranty expenses.

Many forms of product testing only apply one form of stress, which only promotes a subset of all possible failure mechanisms. Basing the MTBF calculation on a single stress test, while possible to be accurate enough for use, is oft en missing important life-cycle conditions, stresses and failure mechanisms.

The simple issue here is that the

internal definition of failure may be different than your customers’ definition. Be clear and concise, plus open to new definitions of failure. However, it is generally limited to product specifications.

History of Use

Karl Pearson first mentions the “negative exponential distribution” in 1895. The Exponential Distribution has a number of interesting properties, one of which takes advantage of the tools available in the 1950’s and 60’s. One specifi cally interesting property is the ability to add failure rates. Adding was rather easy at the time using mechanical and later electric adding machines. However, using a slide rule and tables for the exponents is cumbersome with possibly 100’s or 1,000’s of calculations needed.

Figure 7 - The Old Method of Calculating MTBF

R(t) = R1(t) R2(t) ... Rn(t)

R(t) = e-λ1t e-λ2t ...e-λnt

R(t) = e -(λ1 + λ2 + ...λn)t

Figure 8 - The Exponential Distribution permits adding failure rates

In 1961, the fi rst issue of the MIL-HDBK-217 (Defense 1992) detailed how to perform parts count predictions. The method relied on the ability to add failure rates, as shown in fi gure 7. Work continues to this day to update and revise the methodology (Gullo 2009). These eff orts may take us out of the era of mechanical adders, as today doing complex calculations is as easy as turning the crank.

Today we have models and distributions for the complex array of failure mechanisms and should take advantage of this knowledge. Limiting the combination of failure rate information to a constant for each component distorts and misleads those attempting to make decisions based on the prediction or data analysis.

Examples of Misuse of MTBF

While it is a convenient assumption to say the component, product or system has a constant failure rate, this is oft en not true. And, this assumption does lead to very poor understanding, modeling and decisions related to real products. The obvious misuse stems from the various ways that individuals misunderstand MTBF. For example, if an electrical engineer believes MTBF to be a failure free period, his selection of components will have a signifi cantly less desirable fi eld failure rate. I, and many of the reliability professionals I’ve spoken to, have also run across this common misunderstanding in the engineering community. Simply confi rm a common understanding of the terms being used.



Reliability Review (cont.) McLinn - A Short History of Reliability - continued from page 18.

28. Wallodi Weibull, “A Statistical D i s t r i b u t i o n F u n c t i o n o f Wi d e Applicability”, ASME Journal of Applied Mechanics, Vol. 18(3), pp.293-29729. Bosch Automotive Handbook, 3 rd edition, 1993, p15930. David Taylor Research Center, Carderock Division, January 198831. This Mechanical Reliability Document is NSWC 92/L0132. Reliability and Maintainability Guideline for Manufacturing Machinery and Equipment, M-110.2, Issued by the SAE, Warrendale, 199333. Mechanical Applications in Reliability Engineering, RBPR -1 through -6, RAC, Rome, New York, 199334. Vallabh Dhudsia, Guidelines for Equipment Reliability, Sematech document 92031014A, published 199235. This short lived journal was called “Communicat ions in Rel iabi l i ty, Maintainability, and Supportability”, ISSN 1072-3757, SAE, Warrendale, 1994

James McLinn CRE, ASQ Fellow is a Reliability Consultant with Rel-Tech Group in Hanover, Minnesota. He has over 30 years experience with reliability topics and has authored three monographs and more than 30 papers on reliability. Rel-Tech has 13 training classes in reliability. He can be reached at [email protected]

Raheja - Death of a Reliability Engineer - continued from page 7.the professional death of the reliability engineer is likely. This is one place the constant failure rate applies. Not to the product. To the reliability profession!Actually there is nothing wrong with the reliability profession. The problem lies with the professors. They hardly teach reliability in the engineering school. Among those who teach reliability, they tend to emphasize applied statistics instead of robust design. I hope the universities will do something positive; not only to prevent the death of a reliability engineer, but also, the death of a design engineer who is responsible for reliability.

Misuse of FMEAs still abounds. Most reliability engineers still use it aft er the design is released instead of doing them at the concept stage. They still use FMEAs for predicting the probability of failure and designing test plans instead of designing for zero critical failures. The Fault Tree Analysis is still used sparingly for design improvements.

Dev Raheja is an International Reliability Consultant from Bal-timore Maryland. He originally wrote this article for the March 1990 edition of Reliability review. With over 30 years experience in reliability, topics he has a unique perspective. He is the co-author of Assurance Technology Principles and Practices:Product, Process and Safety Perspective, A fellow of ASQ, he can be reached at [email protected]


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Student Paper Contest Details

The Reliability Division of the American Society for Quality will host a student paper competition on topics of reliability in 2010.

Three prizes will be awarded (First - $300, Second-$200 and Third-$100) -

To be eligible for the competition the student:

1) Shall be enrolled at least half time in a university anywhere in the world. Current transcript required to be submitt ed with paper. Student may have any major.2) Paper shall be at least 6 pages in length and no more than 10 pages when single spaced and 10 point font in the Word™ format and in English.3) Topic for papers may be broadly reliability related including such diverse related topics as statistical applications for reliability, maintenance topics, safety, six sigma/lean, soft ware reliability or risk. Case studies, applied work or student projects are eligible.4) The First place paper will be published in the Reliability Review. Some editing may be required to fi t that format.5) All other papers will be considered for publication regardless of placement. Papers can’t have been published in a refereed Journal previously.6) Deadline is May 15, 2010 for submission of entries. Entries should be sent to [email protected], editor of the Reliability Review.

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Craney - continued from page 3.may fi nd it interesting, as you read through it, to realize that, in some cases, not much

has changed.This issue offers you a historical perspective, a historical paper, and a view on a historical

metric for reliability. Some of you may agree with these authors, while others may not. If you have comments, differing views, or praise for a necessary point that has been made, please consider a letter to the editor. I was at RAMS in January and had a chance to talk to many of our Reliability Review readers. I had a lot of interesting discussions about recent articles in the journal, as well as various comments about the journal itself (format, size, electronic vs. hard copy, etc.). This journal is here to serve you and other R&M professionals, so if you have a point you’d like to make or a topic for discussion, write it down and send to me ([email protected]).


ORDER FORM FOR: _______________________________________C/O_________________________

Delivery Address _______________________________________________________Email Address ____________________________________________________________ >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

PUBLICATION QTY. PRICE TOTAL

(NEW) Develop Reliable Software at Low, Low Life Cycle Cost with Upgraded Reliability Engineering Practices ______________ ______ x $25.00 = ______(NEW) Homeland Security & Reliability, An Airport Model ______________ ______ x $25.00 = ______

Design For Reliability by X.Tian ______ x $30.00 = ______

Practical Weibull Analysis (NEW, 5th Edition) ______ x $30.00 = ______ Mechanical Design Reliability Handbook ( NEW, 2nd edition) ______ x $25.00 = ______

Practical Accelerated Life Testing ______ x $30.00 = ______

Credible Reliability Prediction ______ x $25.00 = ______

SUB - TOTAL $ _______

S & H each book: (In North America $7.00 fi rst copy and $3.00 for each additional copy. For International orders, email [email protected] for S&H charges) X qty. books ____ Add $_________ PLEASE ENCLOSE U.S. CHECK (Made out to Reliability Division) FOR TOTAL $ __________ [ ALSO MONEY ORDERS, BUT NO CREDIT CARDS ] SEND ORDER TO: James McLinn, Reliability Division, 10644 Ginseng Lane, Hanover, Minnesota 55341 USA

RELIABILITY DIVISION ASQPublications Offi ce

email: [email protected]

RELIABILITY DIVISION MONOGRAPHS ARE DESCRIBED ON THE FOLLOWING PAGES. THEY ARE ALL CURRENTLY AVAILABLE AND EACH IS LISTED ON THE ORDER FORM BELOW FOR YOUR CONVENIENCE. PLEASE INDICATE QUANTITY DESIRED OF EACH SELECTION, TIMES PRICE FOR EACH AND SUM COLUMN FOR SUB-TOTAL. ADD DELIVERY CHARGES $7.00 FOR NORTH AMERICAN AND $10.00 FOR WORLD WIDE. COMPUTE THE TOTAL AMOUNT TO BE REMITTED WITH YOUR ORDER:

Another simple issue is the advertising of product or component reliability by simply stating an MTBF value. Without stating the conditions, environment, usage period, and other reliability related bits of information, the reader is left to wonder what the MTBF really means. For a component that has an increasing failure rate over time, like a cooling fan that experiences bearing wear out, the MTBF is a valid approximation of the fan failure rate over some specific period of time. The fan datasheet oft en does not state the expected duration over which the constant failure rate applies. If the vendor is designing and evaluating fan life for an expected one year of use, then the life data may actually be exponential. If the application that the electrical engineer is considering requires a cooling fan to operate for 10 years, then he may be surprised when the product qualifi cation or field performance experiences higher than expected fan failures.

Summary

This paper illustrates a few of the mistakes or issues around the common misunderstandings of MTBF. Being aware of these issues helps the reliability professional guard against serious errors when using MTBF. Additionally, knowing when to use MTBF and when not to use it for reliability tracking or analysis is beneficial. Future papers will explore how to best spot misuse and what to do about the issue. Awareness is the first step.

Footnotes

1. Ireson, Grant, Coombs, Clyde and Moss, Richard, Handbook of Reliability

Engineering and Management, McGraw-Hill, New York, 19952. Military Handbook 217, rev. F, Reliability Prediction of Electronic Equipment, published by the Deaprtment of Defense, 19923. Donald L Klipstein, J. The Great Internet Light Bulb Book, Part I, Retrieved 2/12/2010, from fi le:///Users/fms/Documents/Books/ReliabilityPrediction/The%20Great%20Internet%20Light%20Bulb%20Book,%20Part%20I.webarchive, published, 20064. Lou Gullo, “The Revitalization of MIL-HDBK-217.” Retrieved 2/12/2010, from http://www.ieee.org/portal/cms_docs_relsoc/relsoc/Newslett ers/Sept. 2008/Revitalization of MIL-HDBK-217.htm, 20095. Fred Schenkelberg, “Trapped by MTBF - A Study of Alternative Reliability Metrics”, International Applied Reliability Symposium, San Diego, ReliaSoft Corporation, 2007

Fred Schenkelberg is has 20 years experince in reliability with a variety of products. He is currently a consultant with Ops Ala Carte in San Jose. During his career, Fred has experienced many problems with the mis-use of MTBF. He can be reached at [email protected]

The Perils of MTBF (cont.)


R & M Monographs

Mechanical Design Reliability Handbook (NEW Second Edition)by James A. McLinn, CRE, ASQ Fellow

Mechanical Design Reliability Handbook (Second Edition) Expanded to 92 pages. Mechanical design reliability has been a sparsely covered topic. This monograph is instructive to practical engineers desiring to understand and test materials and mechanical systems. It fi rst addresses the concepts of stress, strain, tension, shear and material fatigue. The elastic limits and plastic deformation is modeled as well as creep situations. Accelerated life, Miner’s rule and non-normal material strength and variable load distributions are modeled and illustrated in the 92 pages. Available at $25.00 each, plus postage. Use RD Pubs Order Form in this edition.

Practical Weibull Analysis (NEW Fifth Edition) by James A. McLinn, CRE, ASQ Fellow

New and improved! This monograph presents practical discussion and examples of essential Weibull topics. Most textbooks on this subject require extensive statistical background. This book was designed to be direct and to the point. In 75 pages it leads the reader quickly through the principles of Weibull. The useful examples and Weibull graphs illustrate applications such as confi dence calculations, non-straight lines, optimum replacement costs, maintainability, and analysis of accelerated life tests and multiple stress tests. Just $30.00 each, plus postage. Use the Order Form in this edition.

Credible Reliability Predictionby Laurence George, Ph.D., ASQ Fellow

This monograph extends MTBF prediction to predict the age-specifi c reliability of redundant, stand-by, complex, and life-limited systems. The method uses fi eld reliability data and proportional hazards models. Data are from older, comparable products, because product generations have similar reliability functions despite changes. Price is $25.00 each, plus postage. Use the order form. Electronic version available.

Practical Accelerated Life Testingby James A McLinn, CRE, ASQ Fellow

A 125 page book that simply and uniquely delineates the key steps and guidelines for setting-up and administrating accelerated life tests. In eight sections it covers a brief history of accelerated methods, applications of the techniques, guidelines for test selecting test environments, common test methods, practical guidelines for test set-up, key parameters to monitor, sample size decisions, models for analysis and examples of analysis of diffi cult results. Important guidelines and pitfalls to avoid are given. Examples include multiple level tests and step-stress tests. Just $30.00 each, plus postage. Use the Order Form in this edition.

R & M MonographsPractical Develop Reliable Software at Low, Low Life Cycle

Cost with Upgraded Software Reliability Engineering (NEW)

by Norman F. Schneidewind, Ph.D. with related articles by Samuel J.Keene, Ph.D.

The contents of this publication present for software engineers, reliability engineers and software quality specialists, and managers practical tools and methods which the authors have perfected and applied in a broad range of enterprises. They includestrategy and tactics for improvement of the software engineering process , software reliability models, development of trustworthy code, and reliability assessment throughout the product life cycle. Software Reliability Topics. Available for purchase now: Price is $25.00 plus Shipping and Handling; see the order form. Use the Order Form in this edition.

Design for Reliabilityby Xijin (Bill) Tian, Ph.D. and contributions from

Drs. L.L. George, S.J.Keene, T.Craney, and J.McLinnThis new monograph contains the entire series written over the past eighteen months by Dr. Tian plus much more. The authors clearly describe practical methods they employ in effectively ensuring that high reliability goals are achieved. They integrate reliability improvement practices and methods congruent with project design rules. The additional chapters present relevant material by Drs. Larry George, Sam Keene,Trevor Craney, and James McLinn. The contents offer a practical Benchmark resource for reliability and maintainability engineers. Price is $30.00 plus Shipping and Handling. Use the order form in this edition.

Order Now!

Homeland Security and Reliability, An Airport Model (NEW)by Norman F. Schneidewind, IEEE Congressional Fellow,

IEEE Fellow andProfessor of Information Sciences, Naval Postgraduate SchoolDr. Schneidewind’s model presented in this monograph addresses the airport security problem. It facilitated his specifi c recommendations to the U.S. Congress for legislative or management action to close the security loopholes. Model quantitative results are used to delineate the implications for changes in security policy at the nation’s airports. The work presents solutions which may be extended to other security settings.

Price is $25.00 plus shipping and Handling. Use the Order form in this edition. Order Now!

Coming Soon - Systems Reliability Monograph

Reliability Review Vol. 30, March 2010 Page 32

RELIABILITY DIVISIONAmerican Society for Quality

www.asq.org/reliability/The RELIABILITY DIVISION is a major professional specialty association within the framework of the American Society for Quality. Its members have a particular expertise and interest in reliaiblity and related disciplines. Division activities are concerned with reliability, maintainability, quality, safety, and effectiveness of products, processes, and services and with related topics such as product liability and risk management.

Any ASQ member may choose to become a division member. Annual dues for the Reliability Division is $9.00. Members receive the Reliability Review and are eligible to receive other benefi ts as offered by the Division. Participation in Division activities provides excellent and unique opportunities for professional growth and service.

RELIABILITY DIVISION OFFICERS (2009-2010)

REGIONAL COUNCILORS

Region 15 CouncilorH.M. Wadsworth, Ph.D.

660 Valley Green Dr. N.E.Atlanta, GA 30342

(404) [email protected]

Region 3 CouncilorTBD

Vacant

Region 4 CouncilorTim Yaworski

Business Analyst-6SigmaDominion Exploration

Calgary, Alberta T3J 4P8(403) 650-0254

[email protected]

Region 11 CouncilorHugh Broome

3 Sweetwater Dr.Johnson City, TN 37615


Region 7 CouncilorJohn A. Miller

6202 Sonoma DriveHuntington Beach, CA 92647


Vice Chair MembershipTrevor Craney

Shell Unconventional Oil PO Box 481

Houston, TX 77001-0481 (713) 245-8064

[email protected]

Region 1 CouncilorMike Malcos, MS58

C.S. Draper Lab555 Technology Sq.,

Cambridge, MA 02139(617) 258-3255

[email protected]

Region 14 CouncilorJames Chris Deepak

HalliburtonHouston, TX 77032


Region 2 CouncilorDavid Auda

50 North Forest Rd.Williamsville, NY 14221


Region 10 CouncilorRajinder Kapur

Q. Mgr. Gehring Group179 Pheasant RunTroy, MI 48098(248) 703-7148

Region 6 CouncilorDawn Onalfo

SBC Services Inc.3080 Mills Dr.

Brentwood, CA 94513(925) 824-6988

Region 25 International Councilor

Deepak Dave Kansas City, MO 64153


Region 12 CouncilorKen Schmidt

Optim Associates Inc.www.opticorp.com

[email protected]

Region 8 CouncilorBrad Nelson

Netco G.S.,Sre 25013665 Dulles Tech.Dr.

Herndon, VA 20171(703) 480-2617

[email protected]

Vice Chair SectionsDavid Auda

50 North Forest Rd.Williamsville, NY 14221


Past ChairJames McLinn

10644 Ginseng LaneHanover, Minnesota


Region 13 Councilor Deepak Dave

Harleyy Davidson Mtr. Co. Kansas City, MO 64153


Region 9 CouncilorRichard I. Coy

12616 Markay Dr. Fishers, Indiana 46038


Region 5 CouncilorFred Smith

Tyco Electronics Corp.LDFSS. Engineering Dev.

Harrisburg, PA 17105(717) 586-8049

[email protected]

ChairFred Schenkelberg

15466 Los Gatos,Blvd..Los Gatos, CA 95032

(408)[email protected]

SecretaryJohn Bowles,, Ph.D.

Univ. of South Caarolina(803) 777-2689

[email protected]

TreasurerAlfred M. Stevens200 Cordoba Ct.

Merritt Island, Fl 32953H 321 453-6628

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