Content By Fred Schenkelberg

Fred Schenkelberg’s picture

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?

Fred Schenkelberg’s picture

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.

Fred Schenkelberg’s picture

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.

Fred Schenkelberg’s picture

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.

Fred Schenkelberg’s picture

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.

Fred Schenkelberg’s picture

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?

Fred Schenkelberg’s picture

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.

Fred Schenkelberg’s picture

By: Fred Schenkelberg

When products were crafted one at a time, the design and manufacturing processes were often done by the same person. For example, a craftsman would design and build a chest of drawers or a carriage. Some trades would employ apprentices to learn the craft, which also included design.

Fred Schenkelberg’s picture

By: Fred Schenkelberg

What if all failures occurred truly randomly? Well, for one thing the math would be easier.

The exponential distribution would be the only time to failure distribution—we wouldn’t need Weibull or other complex multi-parameter models. Knowing the failure rate for an hour would be all we would need to know, over any time frame.

Fred Schenkelberg’s picture

By: Fred Schenkelberg

Concurrent engineering is a common approach that pairs developing the product design and its supporting manufacturing processes through the development process. There are several reasons why this is a good idea.