CRE Preparation Notes

Prep notes for ASQ Certified Reliability Engineer exam ISSN 2165-8633

The idea of the CRE Preparation Notes series is to provide you short practical tutorials on all the elements that make up the ASQ CRE body of knowledge. The articles provide introductionary material, basics, how-to’s, examples, and practical use guidance for the full range of reliability engineering concepts, terms, tools, and practices.

Keep your knowledge fresh with regular review of topics and tools that make up reliability engineering.

- Improve your reliability engineering skills
- Learn about the wide range of tools available
- Enhance your resume with the ASQ CRE

You will find the most recent tutorials in reverse chronological order below. Below each article is the section and specific clause of the CRE Body of Knowledge that tutorial addresses. Click on those tags to find other articles on the same topic. To the right on the sidebar, there is a listing of the 7 major categories in the body of knowledge - it's a quick way to find groups of articles on each specific area. You can also use the search function to locate articles, podcasts, or tutorials on specific topics.

by Dennis Craggs Leave a Comment

Extended Bogy Testing

Introduction

Extended bogy testing builds on test to bogy (TTB), discussed in a prior article. TTB focused on calculating the number (N) of parts tested to one life bogy, with 0 failures allowed, to a specified reliability (R) and confidence (C) levels.

Using TTB to verify conformance to high reliability and confidence targets requires very large sample sizes, increasing testing cost. The capacity to test large samples may require large facility capital expenditures. Also, the zero failures allowed paradigm removes the opportunity to learn about product failure modes and the opportunity to improve the product through design or manufacturing process changes.

This article focuses on extended bogy test plans as an alternative to TTB.

[Read more…]

by Fred Schenkelberg Leave a Comment

The Non-parametric Friedman Test

The Friedman test is a non-parametric test used to test for differences between groups when the dependent variable is at least ordinal (could be continuous). The Friedman test is the non-parametric alternative to the one-way ANOVA with repeated measures (or the complete block design and a special case of the Durbin test). If the data is significantly different than normally distributed this becomes the preferred test over using an ANOVA.

The test procedure ranks each row (block) together, then considers the values of ranks by columns. The data is organized in to a matrix with B rows (blocks) and T columns (treatments) with a single operation in each cell of the matrix. [Read more…]

by Fred Schenkelberg Leave a Comment

Continuous Improvement of the Risk Management Framework

Systems and processes exist in our dynamic world. Each organization and situation is different. Just as there is not one risk management process that works for any organization, there also is the need for continuous improvement of an existing system.

When first designing a risk management process for your organization, you consider your objectives and adjust a framework to fit your needs. Over time your objectives and the surrounding environment changes, thus requiring a critical look at your process. [Read more…]

by Fred Schenkelberg 2 Comments

How to Estimate the Number of Failures Next Month

Let’s say you have shipped 1,000 products to your customer on January 1^st. All are immediately placed into service. And each month since you have received a few product returns, what we are going to call failures. We also have fitted the data to a Weibull distribution. Then in May, your boss asks you to estimate how many failures to expect in June.

This is a simple example as we’re not shipping units every month, nor changing the product design or assembly process. We also have worked out the fitted Weibull parameters already. That leaves the calculation of how many failures we should expect over the next month. [Read more…]

by Fred Schenkelberg 2 Comments

Determine Success Testing Sample Size

“How many samples do we need?” is a very common question. It is one you will receive when planning nearly any kind of reliability testing. It is a great question.

Having too few samples means the results are likely not useful to make a decision. Too many samples improve the results, yet does add unnecessary costs. Getting the right sample size is an exercise starting in statistics and ending with a balance of constraints.

There are six elements to consider when estimating sample size. We will use the success testing formula, a life test with no planned failures, to outline the necessary considerations. [Read more…]

by Fred Schenkelberg Leave a Comment

The Basics of Planned and Deferred Maintenance

We can plan to do more than we are capable of accomplishing. The remaining items, if they warranty accomplishing become deferred. They roll over to the next’s day’s list of actions to take.

Of course, in practice, the process to plan, execute, and defer maintenance activities is a bit more complex than described above. The ability to maintain equipment in working order along with minimizing downtime and costs is in large part the balance between resources available to conduct maintenance and the increased risk of system failure due to deferred maintenance. [Read more…]

by Fred Schenkelberg Leave a Comment

Data Outliers and Questions

When looking at a pile of data, sometimes there is a data point that is not like the others. It attracts attention as it is different than the rest of the data.

When I spot something odd in a dataset, I wonder if there is something to learn here. Is this an opportunity to make a discovery or improve a process?

All too often it is tempting to remove the outlier as a mistake. Or to drop the outlier as it doesn’t make any sense and ‘messes up’ the analysis. [Read more…]

by Fred Schenkelberg Leave a Comment

The Detail Necessary in a Risk Management Plan

A risk management plan has to meet your organizations needs as the organization identifies, manages, and mitigates potential and actual risks. The ISO 31000 framework does not detail how an organization should plan or what elements are required for an effective plan.

This short article outlines a few details that may comprise a starting point as you build a risk management plan within your organization. Or, the suggestions here may help you review and improve your existing plan. [Read more…]

by Fred Schenkelberg Leave a Comment

McNemar Test

The NcNemar Statistical Test

The McNemar test is a nonparametric statistical test to compare dichotomous (unique) results of paired data.

If you are comparing survey results (favorable/unfavorable) for a group of potential customers given two ad campaigns, or evaluating the performance of two vendors in a set of prototype units, or determining if a maintenance procedure is effective for a set of equipment, this test permits the detection of changes.

The McNemar test is similar to the χ² test. The McNemar only works with a two by two table, where the χ² test works with larger tables. The χ² test is checking for independence, while the McNemar test is looking for consistency in results.

Let’s examine an example where a group of people are surveyed about a prototype design, before and after a presentation. [Read more…]

by Fred Schenkelberg Leave a Comment

The 3 Parameter Triangle Distribution 4 Formulas

This is part of a short series on the common distributions.

The Triangle distribution is univariate continuous distribution. This short article focuses on 4 formulas of the triangle distribution.

The distribution becomes a standard triangle distribution when a = 0, b = 1, thus it has a mean at the $- \sqrt{{c}/{2}\;} -$ and the median is at $- 1-\sqrt{{\left( 1-c \right)}/{2}\;}-$. The distribution becomes a symmetrical triangle distribution when $- c={\left( b-a \right)}/{2}\;-$.

The triangle distribution is used to approximate distributions when the actual distribution is unknown and bounded, often useful for Monte Carlo simulations. Other applications include subjective representation when there is evidence of bounds and a mode, or as a substitution to the beta distribution since it is bounded. [Read more…]

by Fred Schenkelberg Leave a Comment

The 2 Parameter Uniform Distribution 7 Formulas

This is part of a short series on the common distributions.

The Uniform distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Uniform Distribution. A common application is as a non-informative prior. Another application is to model a bounded parameter. The uniform distribution also finds application in random number generation. [Read more…]

by Fred Schenkelberg 1 Comment

The 1 Parameter Poisson Distribution 4 Formulas

This is part of a short series on the common life data distributions.

The Poisson distribution is a discrete distribution. This short article focuses on 4 formulas of the Poisson Distribution. It is also known as the rare event distribution. It has application in a homogeneous Poisson princess and with renewal theory. [Read more…]

by Fred Schenkelberg Leave a Comment

The 2 Parameter Pareto Continuous Distribution 7 Formulas

This is part of a short series on the common life data distributions.

The Pareto distribution is a univariate continuous distribution useful when modeling rare events as the survival function slowly decreases as compared to other life distributions. This short article focuses on 7 formulas of the Pareto Continuous Distribution also known as the Pareto distribution of the first kind (there are three kinds, apparently). [Read more…]

by Fred Schenkelberg Leave a Comment

The Quality Triangle and Reliability

The quality triangle is an admission that every project has design constraints. Note that reliability is rarely listed directly within the triangle, yet reliability does impact each element of the triad.

In a world where the design team is beset with numerous design for X priorities, understanding how reliability related to the top three and most common set of constraints is imperative.

For any set of priorities, you should be able to convey how addressing reliability performance in the design and assembly process impacts those priorities. Addressing reliability can improve quality, reduce the risk of launch delays, and reduce the cost of a product. All this and improve customer satisfaction as well.

Let’s explore a few ways you can connect reliability priorities to the common quality triangle priorities. [Read more…]

by Fred Schenkelberg Leave a Comment

The 2 Parameter Binomial Discrete Distribution 4 Formulas

This is part of a short series on the common life data distributions.

The Binomial distribution is discrete. This short article focuses on 4 formulas of the Binomial Distribution.

It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]