Content By Fred Schenkelberg

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By: Fred Schenkelberg

Reliability activities serve one purpose: to support better decision making. That is all they do. Reliability work may reveal design weaknesses, which we can decide to address. Reliability work may estimate the longevity of a device, allowing decisions when compared to objectives for reliability.

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By: Fred Schenkelberg

Mean time between failures (MTBF) is a symptom of a bigger problem. It’s possibly a lack of interest in reliability (which I doubt is the case). Or it’s a bit of fear of reliability.

Many shy away from the statistics involved. Some simply don’t want to know the currently unknown. It could be the fear of potential bad news that the design isn’t reliable enough. Some don’t care to know about problems that will requiring solving.

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By: Fred Schenkelberg

MTBF use and thinking is still rampant. It affects how our peers and colleagues approach solving problems, and there is a full range of problems that come from using the “mean time between failure” (MTBF) metric.

So, how do you spot the signs of MTBF thinking even when MTBF is not mentioned? Let’s explore some approaches that you can use to ferret out MTBF thinking and move your organization toward making informed decisions concerning reliability.

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By: Fred Schenkelberg

The term “Weibull” in some ways has become a synonym for reliability. Weibull analysis = life data (or reliability) analysis. The Weibull distribution has the capability to describe a changing failure rate, which is lacking when using just mean time between failures (MTBF). Yet, is it suitable to use Weibull as a metric?

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By: Fred Schenkelberg

Our customers, suppliers, and peers seem to confuse reliability information with mean time between failure (MTBF). Why is that?

Is it a convenient shorthand? Maybe I’m the one confused, maybe those asking or expecting MTBF really want to use an inverse of a failure rate. Maybe they aren’t interested in reliability.

MTBF is in military standards. It is in textbooks and journals and component data sheets. MTBF is prevalent.

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By: Fred Schenkelberg

A conversation the other day involved how or why someone would use the mean of a set of data described by a Weibull distribution.

The Weibull distribution is great at describing a dataset that has a decreasing or increasing hazard rate over time. Using the distribution we also do not need to determine the mean time between failures (MTBF)—which is not all that useful, of course.

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By: Fred Schenkelberg

If you have been a reliability engineer for a week or more, or worked with a reliability engineer for a day or more, someone has asked about testing planning. The questions often include, “How many samples?” and, “How long will the test take?” No doubt you’ve heard the sample-size question.

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By: Fred Schenkelberg

We establish reliability goals and measure reliability performance. Goals and measures can be related; however, they’re not the same, and neither do they serve the same purpose.

Recently, I’ve seen a few statements that seem to confuse the role of statistical confidence when establishing a goal. Thus, I’d like to relate how I think about the difference between goals and statistical confidence, along with how they are related.

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By: Fred Schenkelberg

Spending too much on reliability and not getting the results you expect? Just getting started and not sure where to focus your reliability program? Or, just looking for ways to improve your program?

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By: Fred Schenkelberg

A fault tree analysis (FTA) is a logical, graphical diagram that starts with an unwanted, undesirable, or anomalous state of a system. The diagram then lays out the many possible faults, and combinations of faults, within the subsystems, components, assemblies, software, and parts comprising the system that may lead to the top-level unwanted fault condition.