Your Reliability Engineering Professional Development Site
A series of articles devoted to the eradication of the misuse of MTBF.
ISSN 2168-4375
Plus, we explore other commonly misused or misunderstood reliability-related topics and what one should do instead. A little understanding will help you get better results with your efforts.
Note: This is a reposting with editing, updating, etc. of the articles that first appeared at NoMTBF.com.
The following is a recent discussion on the sister Linkedin NoMTBF Group. It was and may continue to be a great discussion. Please take a look and comment on where you stand. Do you have some form of the Arrhenius reaction rate equation in your reliability engineering work? Join the discussion here with a comment or on the Linkedin group conversion.
Fred
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An excellent short white paper by Craig Hillman that is worth reading. It underscores why I claim HALT is the second worst 4 letter acronym in our profession.
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Hello, can anyone advise me how to calculate temperature acceleration factor for a complex system including cards, RF elements, cables, motors and moving parts? Is the Arrhenius model valid for such systems, or there are more precise models? Thank you!
[Read more…]Fred i was asked this question and wanted to know what your thoughts were on this. R and D asked me what was the criteria to decide if to test at a component level or at a system level , my answer was that it should depend on what is the reliability and confidence level of the component
your thoughts?
thanks
sd
Just a short post to point to a paper on the accuracy of part count prediction techniques. A few years ago, I recalled seeing a paper that studied the difference between various parts count methods and actual results.
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Historically, Reliability Engineering of Electronics has been dominated by the belief that 1) The life or percentage of complex hardware failures that occur over time can be estimated, predicted, or modeled, and 2) the Reliability of electronic systems can be calculated or estimated through statistical and probabilistic methods to improve hardware reliability. The amazing thing about this is that during the many decades that reliability
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With the kind permission of Wayne Nelson and Robert Abernathy, we are posting an article on the analysis of repair data. As you may know, the assumptions made when using simple time-to-failure analysis of repairable systems may provide misleading results. Using the analysis method outlined by Wayne is one way to avoid those costly mistakes.
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When conducting a Human Reliability Assessment (HRA), we use the terminology errors of commission or errors of omission. It behooves every professional to question why we focus on one metric in preference to all others in an objective and constructive manner in order to discern whether we are exposing our organization to errors of professional omission or commission. The other conclusion is that we are doing the right thing and this is also an empowering piece of knowledge.
Giving a presentation last week and asked if anyone uses an 85%RH/85°C type test, and a couple indicated they did. I then asked why.
The response was – just because. We have always done it, or it’s a standard, or customers expected it. The most honest response was, ‘I don’t know’.
Why is the test being done? Who is using the information for a decision? What is the value of the test results? If ‘just because’ is the best you can say about a test, why do it?
I’ve often railed on and on about the inappropriate use of MTBF over Reliability. The often cited rationale is, “it is simpler”. And, I agree, making simplifications is often necessary for any engineering analysis.
It goes too far when there isn’t any reason to knowingly simply when the results are misleading, inaccurate or simply wrong. The cost of making a poor decision based on faulty analysis is inexcusable.
During RAMS this year, Wayne Nelson made the point that language matters. One specific example was the substitution of ‘convincing’ for ‘statistically significant’ in an effort to clearly convey the ability of a test result to sway the reader. For example ‘the test data clearly demonstrates…’
As reliability professionals let’s say what we mean in a clear and unambiguous manner.
Thus, you may suspect, this topic is related to MTBF.
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I am a rock climber. Climbing relies on skill, strength, knowledge, luck, and sound gear. Falling is a part of the sport, and with the right gear, the sport is safe. So far, I’ve enjoy no equipment failures.
I do not know, nor want to know, the MTBF (or MTTF) of any of my climbing gear. I’m not even sure this information would be available. And, all the gear I use has a finite chance of failing every time the equipment is in use. Part of my confidence is that the probability of failure is really low.
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The classic formula for availability is MTBF divided by MTBF plus MTTF. Standard. And pretty much wrong most of the time.
Recently, working for a bottling plant design team, we pursued design options to improve the availability and throughput of the new line. The equipment would remain the same: filler, capper, labeler, etc. So we decided to gather the last six months or so of operating data, which included up and down time. Furthermore, the data included time to failure and time to repair information.
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Let’s look at the characteristics of a sound reliability metric and how MTBF is not true or beneficial. A metric should be true, beneficial, and timely. We’ll start with a rock climbing analogy.
A bolted hanger along a rock climbing route is often a welcome site. It provides the climber safety (clipping the rope to the bolt), direction (this is the way), and confidence. Does MTBF as a metric do the same for your organization?
As climbers, we count on the bolts to provide support in case something goes wrong or we need to rest along the route.
A reliability metric is often used in the same way as a climbing bolt. The measure, whether MTBF, Reliability, or Failure Rate, assures that the product’s reliability performance is as expected.
The organization’s profits are or will be safe. The development team uses the measures to guide design and supply chain decisions. The measure provides confidence to the organization regarding meeting customer expectations around reliability.
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Note: This first article in the NoMTBF campaign was published on April 1st, 2009. Thus, we’ve been at this and making progress for a long time and come a long was since starting the NoMTBF campaign. I am looking forward to your comments, contributions, and suggestions.
Fred
At first, MTBF seems like a commonly used and valuable measure of reliability. Trained as a statistician and understanding the use of the expected value that MTBF represented, I thought, ‘Cool, this is useful.’
Then, the discussions with engineers, technical sales folks, and other professionals about reliability using MTBF started. And the awareness that not everyone, and at times it seems very few, truly understood MTBF and how to properly use the measure.
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