Content By Fred Schenkelberg

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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.

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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.

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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.

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By: Fred Schenkelberg

The planning of environmental or reliability testing becomes a question of sample size at some point. It’s probably the most common question I hear as a reliability engineer: How many samples do we need?

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By: Fred Schenkelberg

Why do so many avoid confronting the reality of failure? In plant asset management, we are surrounded by people who steadfastly don’t want to know about nor talk about failures. Yet failure does happen; let’s not ignore this simple fact.

The blame game

Unlike a murder mystery, failure analysis (FA) is not a game of whodunnit. The knee-jerk response to blame someone rarely solves the problem, nor does it create reliability in the workplace.

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By: Fred Schenkelberg

Just, please, plot the data if you have gathered some time-to-failure data, or you have the breakdown dates for a piece of equipment. Any data really. It could be your review of your car maintenance records and notes and dates of repairs. You may have some data from field returns. You have a group of numbers and you need to make some sense of it. Just, please, plot the data.

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By: Fred Schenkelberg

What happens when a product lasts too long? How long is good enough? Every product is different, and our ability to define what’s “long enough” is fraught with uncertainty. If it wears out prematurely, your customers will go elsewhere. If it lasts too long, they won’t need to come back.

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By: Fred Schenkelberg

Control charts provide an ongoing statistical test to determine if a recent reading or set of readings represents convincing evidence that a process has changed from an established stable average. The test also checks sample-to-sample variation to determine if the variation is within the established stable range. A stable process is predictable, and a control chart provides the evidence that a process is stable—or not.