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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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Performing the Right Corrective Action

Performing the Right Corrective Action

Performing the Right Corrective Action

When something doesn’t work as expected, it is a failure. A common response to a failure by an organization is to restore the system or remedy the situation.

Each failure is unique to the product, industry, customer situation, expectations, etc. Selecting the appropriate response or corrective action when confronted with a failure may or may not be obvious.

Selecting the right corrective action depends on the business and legal factors, along with customer expectations.

For a given failure, thinking through the range of possible responses and selecting the right one takes care to meet the various stakeholder’s requirements or expectations. [Read more…]

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The Environmental and Use Manual

The Environmental and Use Manual

Environmental and Use Manual

How well can you describe the use conditions your product will experience?

How well do you need to know the use conditions?

For some situations, the environment for your product is assessable, others are not. For some situations, we guess the range of expected stresses, others we measure.

The design process and the myriad decisions that impact product reliability rely on characterized environmental stresses.

A great place to consolidate how and where customers will use your product (including the relevant stress factors) is in an environmental manual. [Read more…]

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Building and Using a System Reliability Model

Building and Using a System Reliability Model

From the simplest to the most complex system, building and using a reliability model permits the entire team to make better decisions.

Understanding and monitoring system reliability involves knowing both:

  1. the reliability of elements within the system,
  2. as well as how the elements relate to each other reliability-wise.

We use system reliability models to identify weak links, and focus resources, to meet our desired reliability goals.

Being able to build the right model to meet your team’s needs best is one of your roles as a reliability professional. [Read more…]

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The Many Ways We Use Variance

The Many Ways We Use Variance

The term variance is a statistical concept related to the spread or dispersion of a set of data. Second to the mean, it a common value we may calculate.

We find standard deviation easier to understand and use (it uses the same units as the data) whereas variance uses the units squared.

We use variance in quite a few different ways. Let’s review just a few. [Read more…]

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Asking Questions is Reliability Engineering

Asking Questions is Reliability Engineering

Finding solutions is reliability engineering too.

Have you noticed that finding solutions often requires just the right question, the proper framing of the issue, the query that reveals the problem and solution?

One of the best ways to lead a team and provide a focus on reliability performance is to ask the right questions.

Understanding objectives, risks, and failures is what we primarily do as reliability professionals. We work with teams to achieve or improve reliability performance.

We ask questions. [Read more…]

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Adjusting to Customer Expectations Changing

Adjusting to Customer Expectations Changing

Customers Expectations Tend Toward Better Reliability Over Time

Reliability goals or objectives are just a starting point.

You goals represent your target at one point in time.

At best they represent what your customers expect for reliability performance at one point in time.

When goals are set well, they anticipate what your customer expects when they receive your product. In a perfect world, you customer will find the reliability performance just a bit better than expected.

It’s not a perfect world. [Read more…]

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Introduction to Ongoing Reliability Testing

Introduction to Ongoing Reliability Testing

This type of reliability may have different names. A quick search of a few references in my library and I didn’t find ongoing reliability testing, ORT, in any of them.

It does exist and you may have heard of it before or even use some form of ORT. Or not.

Ongoing reliability testing or ORT is the continued evaluation of your product typically using samples drawn from production. The testing evaluates the reliability performance of recent production units.

The focus is on finding anomalies or changes that may occur in the design, supply chain, or production process that significantly changes field reliability performance. [Read more…]

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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…]