Your Reliability Engineering Professional Development Site
What does this title mean? Chris and Fred discuss how some vendors make ‘startling’ claims regarding reliability from some small amounts of test data. So where does this 60th percentile some of us hear from time to time come from?
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As we celebrate the new year, I am republishing an article I wrote last year, titled “The Future of Reliability Engineering,” as part of the Inside FMEA series. This article applies equally well to FMEA, as you will see.
Sometime in 2023, I will write an article titled “The Future of FMEA.” But, first, I want to hear from readers. Please write me with your ideas on what should be included in the future of FMEA. You can reach me at Carl.Carlson@EffectiveFMEAs.com
Wishing everyone on Accendo Reliability a happy and healthy new year, and best wishes for high reliability and effective FMEAs!
“Destiny is no matter of chance. It is a matter of choice. It is not a thing to be waited for, it is a thing to be achieved.” – William Jennings Bryan
ANSI-IES TM-21 standard method may predict negative L70 LED lives. (L70 is the age at which LED lumens output has deteriorated to less than 70% of initial lumens.) Philips-Lumileds deserves credit for publishing the data that inspired an alternative L70 reliability estimation method based on geometric Brownian motion of stock prices in the Black-Scholes-Merton options price model. This gives the inverse Gauss distribution of L70 for LEDs.
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Dianna and Fred discussing the history and application of published parts count prediction models and standards in reliability analysis.
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Whether new to reliability or a veteran, you have probably heard about the Weibull distribution. It has almost mythical status amongst those who conduct reliability data analysis … or in other words – turning a jumble of dots (data points) into information that actually means something. So why do we ‘worship’ the Weibull distribution? What is so special about it? Whether you have been doing this for a long time or five minutes, you will get something out of this webinar that looks at one of the most popular tools for reliability analysis.
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There are many opinions about the use of the breakdown flag in SAP-PM. I would like to offer my opinion on this debate. First, I do not like the term “breakdown”. With a name like breakdown, nobody will want to check the box! No one likes to admit that a something has failed or is broken. I wish the field were named “reliability event” to take away this negative connotation. I will discuss this in more detail later.
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As we all have learned, eliminating defects is the key to a safe and profitable plant. But how do we know where our ‘bugs” are hiding? There are certainly many ways to uncover where the bugs are, but one place in particular is your computerized maintenance management system (CMMS) or enterprise asset management system (EAM), as it is commonly referred.
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COVID-19 Case Fatality Rate (CFR) is easy to estimate: CFR=deaths/cases. Regression forecasts of COVID-19 cases and deaths are easy but complicated by variants and nonlinearity. Epidemiologists use SIR models (Susceptible, Infectious, and “Removed”) to estimate Ro. These are baseball statistics. Reliability people need age-specific reliability and failure-rate function estimates, by failure mode, to diagnose problems, recommend spares, plan maintenance, do risk analyses, etc. Markov models use actuarial transition rates.
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Age-specific reliability of a standby system depends on components’ failure rates. Reliability computation is interesting when part failure rates depend on age, which is what motivates having a standby system. A Markov chain, approximates the age-specific reliability and availability, which are complicated to compute exactly, unless you assume constant failure rates. Why not use age-specific (actuarial) rates? They are Markov chain transition rates.
[Read more…]Chris and Fred discuss what it means (as a reliability engineer) to try and change something … even though the organization that thinks they are open to change really isn’t. What can we do?
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Below is an email discussion about when Crow-AMSAA modelling can provide believable forecasts.
Carl and Fred discussing how much of what we do as reliability professionals falls into the role of consultants. Learning how to be better consultants will enhance the results of our day-to-day work.
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Suppose installed base or cohorts in successive periods have different reliabilities due to nonstationarity? What does that do to forecasts, estimates, reliability predictions, diagnostics, spares stock levels, maintenance plans, etc.? Assuming stationarity is equivalent to assuming all installed base, cohorts, or ships have the same reliability functions. At what cost? Assuming a constant failure rate is equivalent to assuming everything has exponentially distributed time to failure or constant failure rate. At what cost?
As I rode, I thought, how could I use reliability statistics to optimize a solar-tube production line? Then I noticed a brass glint in the scrub brush. It didn’t look like trash, so I stopped and found an old brass oil lamp like Aladdin’s. Naturally, I rubbed it. There was a flash and a puff of smoke, and out popped the genie who said, “Yes master, by the powers vested in me, I grant you three wishes.”
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