Accendo Reliability

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Accendo Reliability

Reliability Engineering Professional Development
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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.

Sign up for the CRE Preparation Notes email list to receive weekly reliability engineering short tutorials.

- 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.

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Using a Strip Chart

Using a Strip Chart

Using a Strip Chart

Sometimes we just need a simple plot of a few data points. When there is scant data a histogram or box plot just is not informative. This is a great use for a one dimensional scatter plot, dot plot, or a what is called a strip chart in R.

The basic idea is to see where the data lines along a line. For example, let say we have 20 times to first failure. A table of numbers is not all that helpful. We could explore using a cumulative distribution plot (Weibull analysis), yet it would be difficult to fit a distribution with so little data.

Let’s turn to a strip chart to get a look at the data. [Read more…]

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Building a Frequency Table

Building a Frequency Table

Building a Frequency Table

In a meeting the other day, the presenter was talking about a range of different failures for the product in question. She talked about each issue, a bit about the failure analysis, yet didn’t reveal which failures occurred more or less often.

She did provide a handout with a listing of the problems in order of the product field age and listing of the failure name (component or system involved). So, I grabbed a piece of paper to create a frequency table so I could quickly determine which problems occurred more often than others. [Read more…]

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5 Steps to Create a Measles Chart

5 Steps to Create a Measles Chart

Measles Chart Basics

The clever Dr. John Snow mapped cholera cases during the epidemic of 1854 on a street map of the area. This type of mapping now called a measles chart, or defect location check sheet, or defect map, is useful when exploring the effect of location data.

The name measles chart may have come from the habit of using an image of drawing of a product and adding small red dots to signify defect locations.

[Read more…]

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Multi-voting: When More than One Vote is Better

Multi-voting: When More than One Vote is Better

Multi-voting: When More than One Vote is Better

In brainstorming, you may face a large number of unique ideas even after affinity diagraming. Multi-voting is a great tool to gain consensus on the top priority ideas with your team.

For less than 10 or so items we could use a rank ordering method, yet that method becomes cumbersome when there are a lot of items to prioritize. There are a couple of ways to conduct multiple-voting. Mastering this technique will help your team quickly focus on what is truly important. [Read more…]

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Calculating the Probability of a Sample Containing Bad Parts

Calculating the Probability of a Sample Containing Bad Parts

Calculating the Probability of a Sample Containing Bad Parts

Received a question from a reader this morning that will make a nice tutorial.

A box contains 27 black and 3 red balls.  A random sample of 5 balls is drawn without replacement.  What is the probability that the sample contains one red ball?

So here’s my thinking and two ways to solve this problem. Instead of red and black balls in an urn type problem, which is pretty abstract, let’s say we know 3 bad parts are in a bin of 30 total parts.

[Read more…]

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An Introduction to the Cause and Effect Diagram

An Introduction to the Cause and Effect Diagram

An Introduction to the Cause and Effect Diagram

Also called the Ishikawa or fishbone diagram, the cause and effect diagram is a graphical tool that enables a team to identify, categorize, and examine possible causes related to an issue. The intent is to expose the most likely root causes for further investigation.

This diagram is part of a complex failure analysis process when there are many potential causes for the problem or condition. Similar to a brainstorm session about the problem, this graphical tool provides a bit of structure and may prompt additional potential causes.

The Ishikawa diagram is one of the seven basic tools of quality control. [Read more…]

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Mind Mapping for Non-Linear Thinking

Mind Mapping for Non-Linear Thinking

Mind Mapping for Non-Linear Thinking

Sometimes a process doesn’t happen one step at a time. Sometimes, a problem we’re trying to solve has many moving and interacting elements.

Mind Mapping is a technique to capture those scattered thoughts, those non-linear processes, those myriads of interactions and dependencies. It’s a tool that is useful for brainstorming, organizing, and problem-solving. [Read more…]

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Check Sheet: Capture Data Quickly

Check Sheet: Capture Data Quickly

Check Sheet: Capture Data Quickly

There are two common uses for a check sheet. First, to summarize historical data, or second, to collect observations. A byproduct of creating a check sheet is team agreement on definitions of categories and observation procedures.

The investment is minimal other than thinking about how you want to view the data, and what data to collect. [Read more…]

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MSA 2 – Gage Variation

MSA 2 – Gage Variation

Introduction

Most of us rely on accurate measurements. If these measurements are unreliable, then our decisions could be based on false information. How can we have confidence in our measurements?

The purpose of a measurement system analysis is to determine if a gauge is fit for use. This means that we can rely upon the measurements to give us a true indication of the parameter being measured. Our decisions will not be affected by erroneous data. So how can we know the quality of our measurements?
[Read more…]

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Basics of Affinity Diagrams

Basics of Affinity Diagrams

Basics of Affinity Diagrams

When you have a scattering of ideas or suggestions, you can use affinity diagrams to organize and group them.

Affinity diagraming is a simple way for an individual or (better) a group to quickly gather and organize a large number of disparate concepts, ideas, suggestions, etc. It is a natural activity after a brainstorming session. [Read more…]

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Basics of Brainstorming

Basics of Brainstorming

Basics of Brainstorming

Did you know that best idea to solve a problem is often not the first one?

We all have plenty of bad ideas, hunches, guesses, and flawed ideas. That is ok. We also have great ideas and elegant solutions, too. The trick is exploring all those ideas to find the best ones.

One technique to draw out and capture many ideas is called brainstorming. You most likely have heard of and used this tool already. Let’s review a few key elements that make for excellent brainstorming results. [Read more…]

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Extended Bogy Testing

Extended Bogy Testing

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…]

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The Non-parametric Friedman Test

The Non-parametric Friedman Test

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…]

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Continuous Improvement of the Risk Management Framework

Continuous Improvement of the Risk Management Framework

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…]

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How to Estimate the Number of Failures Next Month

How to Estimate the Number of Failures Next Month

How to Estimate the Number of Failures Next Month

Let’s say you have shipped 1,000 products to your customer on January 1st. 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…]