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Fred Schenkelberg — Thought Leader

Author of CRE Preparation Notes, Musings", NoMTBF, multiple books & ebooks>, co-host on Speaking of Reliability>/a>, and speaker in the Accendo Reliability Webinar Series.


This author's archive lists contributions of articles and episodes.

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Introduction to Design for Reliability

Introduction to Design for Reliability

Chapter 7 Design for Reliability of the book Practical Reliability Engineering starts with:

The reliability of a product is strongly influenced by decisions made during the design process.

The key message here is reliability occurs at the point of decision. Each time someone makes a decision, selects a component, chooses a material, assumes a use profile, the eventual product reliability takes shape.

Design for Reliability, DfR, is about making good decisions across the organization concerning reliability. [Read more…]

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When Management Doesn’t Listen

When Management Doesn’t Listen

A Bloomberg articles details the Takata airbag recall series of events. The line that caught my attention is:

…company documents suggesting that Takata executives discounted concerns from their own employees and hid the potential danger…

“Sixty Million Car Bombs: Inside Takata’s Airbag Crisis”, Susan Berfield, et.al. Bloomberg Business Week, posted June 2nd, 2016, https://www.bloomberg.com/news/features/2016-06-02/sixty-million-car-bombs-inside-takata-s-air-bag-crisis

There are other examples where management doesn’t seem to listen when engineers raise concerns. Have we cried wolf too often? Has management gotten used to taken risks as a good business practice?

At times reliability risks are real and need to be clearly communicated. Let’s talk about how you can effectively get the message across. [Read more…]

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Siegel-Tukey Test for Differences in Scale

Siegel-Tukey Test for Differences in Scale

There are a few different reasons we explore differences in scale.

Keep in mind that the scale of a dataset is basically the spread of the data. For most datasets, we’re examining the variance.

Hypothesis tests comparing means vary depending on the assumption of equal variances. Thus testing that assumption requires methods to adequately test the homogeneity of variances. The F-test should come to mind as it is a common approach.

Some datasets do not lend themselves to using the F-test, which is applicable using real numbers. Some datasets gather information that is ordinal or interval data, thus we need another approach to test for differences in scale. [Read more…]

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Hartley’s Test for Variance Homogeneity

Hartley’s Test for Variance Homogeneity

The Hartley test is an extension of the F distribution-based hypothesis test checking if two samples have different variances.

The F test works with two samples allowing us to compare two population variances based on the two samples. This test does not work for three or more populations. We could conduct multiple pairwise comparisons, yet the probability of an erroneous result is significant.

Bartlett’s Test and Levene’s Test are non-parametric checks for homogeneity of variances. Bartlett’s Test pretty much expects the underlying data to be normally distributed.

Levene’s Test is a better choice when you’re not sure the data is normal. Both are conservative and time-consuming to calculate.

We need another way to check for equal variances. [Read more…]

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Introduction to the 6 Sigma Design Approach

Introduction to the 6 Sigma Design Approach

Sigma, σ, is the Greek character we use to represent standard deviation. 6 σ represents the spread of data about the mean. For data with a normal distribution, 6 σ includes 99.7% of the data.

The 6 σ design approach incorporates knowledge of the variation that will occur within the design such that the design has is unlikely to fail.

According to Mikel J. Harry, the foundation of excellence in product quality rests on achieving six sigma product quality. [1]  [Read more…]

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The Sum of Squares Concept

The Sum of Squares Concept

The data analysis course professor tended to focus on the practical application of statistics.

Avoiding statistical theory was fine with me. Learning statistics for me was on how to solve problems, optimize designs, and understanding data.

Then one lecture started with the question, “Why do we sum squares regression analysis, ANOVA calculations, and with other statistical methods?” He paused waiting for one us to answer.

We didn’t. I feared the upcoming lecture would include arcane derivations and burdensome theoretical annotations. It didn’t. [Read more…]

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The Fundamental Set of Reliability Engineering Tools

The Fundamental Set of Reliability Engineering Tools

In a single meeting, you may need to structure a reliability model, create estimates, outline test plans, and discuss a field failure. The breadth of tools and knowledge to be effective is staggering.

No two problems, questions, situations, or industries are the same. Thus, the solutions you provide must differ as well. If you enjoy a complete set of reliability engineering tools at your disposal, you are well situated to address any question.

[Read more…]

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Bartlett’s Test for Homogeneity of Variances

Bartlett’s Test for Homogeneity of Variances

A common assumption when comparing three or more normal population means is they have similar (the same) population variances.

ANOVA and some DOE analysis results rely on the underlying data having similar variances. If this assumption is not true, the conclusions suggested by the ANOVA or DOE may be misleading.

It doesn’t take long to check. [Read more…]

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Plot Your Derating Guidelines

Plot Your Derating Guidelines

The concept of derating is similar to the mechanical engineering concept of a stress–strength analysis.

The intent is to ensure that the selected component or the mechanical design has sufficient strength to withstand the expected applied stresses.

Components operating at or near their rated values have short lives. Consequently, the general practice is to use components for materials well below their rated values to extend the operating life of the items.

This is where derating comes into play. [Read more…]

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Confidence Interval Interpretations and Misunderstandings

Confidence Interval Interpretations and Misunderstandings

Confidence intervals (CIs)provide a means to judge point estimates based on a sample from the population.

If that statement excites you, you may well have the makings of a fine statistician.

CIs are a form of internal estimate and specify a range within which a parameter may reside. CIs helps us evaluate the trustworthiness of point estimates. [Read more…]

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Tolerance Specifications Impact on Reliability

Tolerance Specifications Impact on Reliability

Mechanical drawings and electrical schematics communicate the design.

They provide information sufficient to create a product or build a system.

They provide the necessary details that allow others to construct something that originally was only imaged.

We no longer rely on a single craftsman to build a chest of drawers from concept to delivery.

Instead, they may be a team scattered across many organizations relying on the drawings.

Included on the drawings and schematics are dimensions along with tolerances. A keypad will be a specific width, plus/minus some amount. A resistor is nominally 100 ohms, yet anything between 90 and 110 ohms is fine.

Tolerances acknowledge the variability between individual components or caused during the construction process.

Tolerances also impact the reliability performance of your products.  [Read more…]

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Introduction to Derating

Introduction to Derating

Derating is the selection of components and materials according to a set of standardized safety-margin definitions.

It is used by design engineers to ensure the selected elements of the design do not experience performance problems due to overstress conditions.

Derating, like stress-strength analysis, assists the designer when selecting elements for the product or system.

The outcome is a robust design able to withstand the expected, and some of the unexpected, stress applied. [Read more…]