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A listing in reverse chronological order of articles by:
Working with data, we often choose a model to represent that data. We then use the data to estimate the parameters of the chosen model and we then calculate a confidence interval about the model’s parameters. The confidence interval gives us a numerical assessment of how certain we are, based on our data, of the true value of the distribution parameter we estimated.
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“Most of us spend too much time on what is urgent, and not enough time on what is important.” – Steven Covey
FMEAs take time and cost money. If time is wasted, FMEAs will be unsupported and ineffective.
In this article, I’ll lay out some tips to keep FMEA in-meeting time as short as possible, without reducing the quality of results. Many of these tips are based on lessons learned from doing FMEAs the wrong way. This is not a complete list. I’m sure you have other lessons learned to save time, and would love to hear from you.
In measurement science, “bias” refers to the systematic error component of the measurement system. Unlike other types of measurement error that are randomly distributed, a bias predictably shifts a measurement in the same direction.
For instance, I recently facilitated a “round robin” measurement correlation study with two other companies, where we compared the outputs of our hardness testers using the same set of test samples. While preparing for that study, I realized that one of our hardness testers, on average, tested 1.1 Rockwell B points higher than the reference sample. It wasn’t testing exactly 1.1 points over, but instead ranged from .8 to 1.4 points over across a series of tests. In other words, we had two error components: a bias of +1.1 points and a random error of +/- .3 points. To compensate for this bias, we shifted down the output reading of the tester by 1.1 points, leaving only the random error component in our tester’s output values.
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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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Would you like age-specific field reliability of your products and their service parts? Age-specific field reliability is useful for reliability prediction, diagnoses, forecasting, warranty reserves, spares stock levels, warranty extensions, and recalls. Nonparametric estimation of age-specific field reliability is easy, if you track parts or products by name and serial number for life data. What if there are life limits? What if there’s no life data?
[Read more…]“Interdependent people combine their own efforts with the efforts of others to achieve their greatest success.” Stephen Covey
If you’ve been reading the FMEA facilitation series, by now you understand the primary facilitation skills. Studying and applying these skills will help you achieve excellent results in FMEA applications. [Read more…]
I needed multivariate fragility functions for seismic risk analysis of nuclear power plants. I didn’t have any test data, so Lawrence Livermore Lab paid “experts” for their opinions! I set up the questionnaires, asked for percentiles, salted the sample to check for bias, asked for percentiles of conditional fragility functions to estimate correlations, and fixed pairwise correlations to make legitimate multivariate correlation matrixes. Subjective percentiles provide more distribution information than parameter or distribution assumptions, RPNs, ABCD, high-medium-low, or RCM risk classifications.
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Automotive Electronic parts are qualified before usage in serial production. When product quality and reliability are poor, then assembly problems, high warranty costs, poor service, and recalls occur. The OEM’s and suppliers followed different qualification plans. Ford, GM, and Chrysler and large parts suppliers started the Automotive Electronics Council (AEC) in the 1990’s, with the mission of defining a suite of common qualification tests.
The first qualification standard was Q100, which defined stress tests for electronic components containing integrated circuits. These components are in every electronic control module, some sensors, entertainment systems, safety systems, and, in the future, autonomous driving systems.
“Creativity is an ability to respond adaptively to the needs for new approaches and new products.” Frank Barron
Creativity is an essential ingredient to achieving excellence in FMEAs. Excerpts from the book Effective FMEAs will be used throughout the article.
A version of this article was previously published in the December 2015 edition of Quality Progress magazine.
Last summer while visiting my hometown, I ran into Sam, an old friend who works in a senior technical position for a very large organization. In the course of our conversation, Sam told me about a recent discussion he had with his division’s manager about the possibility of moving up in the company. He was trying to find out if his boss would recommend him for a promotion to a particular supervisory level position that had just opened. His manager’s reply was provocative. He said, “Sam, you’re excellent at your job. I don’t know what we’d do without you. But before I could recommend you for a team leader position, you’re going to have to work on your soft skills.”
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Aside from meeting specific quality system standard requirements—such as those found in ISO 9001 or IATF 16949—well-designed quality system metrics also can serve as meaningful indicators of the strengths and weaknesses of an organization’s various processes.
As a quality manager, I often consider how precisely our quality system objectives and other metrics describe the effectiveness of our systems. One metric I started using recently—overall equipment effectiveness (OEE)—has provided our management team with a composite measurable that encompasses three major branches of our operation: maintenance, production and quality.
[Read more…]The mere formulation of a problem is far more essential than its solution, which may be merely a matter of mathematical or experimental skills. To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advances in science. – Albert Einstein
In this article, we will discuss how to use brainstorming to enhance FMEA effectiveness, and when (and when not) to use brainstorming.
The geometric distribution is a discrete distribution often encountered in reliability work, which contains some valuable properties worth understanding. Let’s take a look at some of these characteristics.
This model is useful when trying to answer the question, “How many trials are needed to get a particular result?” So, the number of trials is not set initially; we need to figure that out.
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How to allocate subsystems’ MTBF requirements with testing? Name-withheld-to-protect-the- guilty proposed “Top-Down” reduction in subsystem MTBF requirements; the more subsystems (in series) that you test, the lower the subsystem required MTBF! “The correct formula is
1/MTBF(subsystem requirement) = 1/MTBF(system requirement) –
((# of subsystems in series – # of subsystems tested)/MTBF(subsystem).”
This “Top-Down…” method is uncited and not found in Internet search.
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The binomial distribution is a discrete distribution useful for estimating the probability of success or failure when these are the only two possible outcomes. Thus, the prefix, “Bi”. Understanding the binomial distribution — how it’s used and under what conditions – is therefore a valuable analytical tool for reliability engineers.
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