Your Reliability Engineering Professional Development Site
When you ‘pass’ a standard-based test, what does that mean? How can you use test results meaningfully besides just noting the product ‘passed’? Understanding the failure mechanisms at play, along with the statistics, is key. [Read more…]
Some may argue that just enough reliability data is just the right amount. Too much may lead to confusion, too little doesn’t inform well. The reliability work we do helps others make decisions, and recent work in how humans make decisions may help us prepare and present our results effectively.
If preparing reliability data-based recommendations, consider using less information. Ed O’Brien and Nadav Klein have found decision-makers tend to use much less data or information to make a decision than they think they will need.
If using data and the derived information to make a decision consider the situation carefully to know when to use a structured decision-making approach or to simply go with your gut. Daniel Kahneman and Gary Klein provide some insights and basic guidelines for decision making. [Read more…]
I have found the best way to lose an audience is to focus on statistical derivation. While this is a fascinating subject for me, it just doesn’t seem to hold an audience’s attention.
Having something interesting and useful to say is key to maintaining an audience’s attention, yet at times how we present helps them become distracted.
So, given great content or proposal, how can you help your audience not quickly check their phone, yet again? [Read more…]
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…]
Graphics, when used well, can convey easy-to-understand information. Reliability graphics may focus on trends in failure rates or the verification of improvement work. Often, reliability graphics convey time-to-failure patterns.
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When a product or system fails, there is a consequence. Often, there is a loss of value. Each warranty replacement costs money. Downtime may result in lost revenue. Do you know the full extent of the costs involved when something fails?
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When we make a measurement, we make a decision based on the data provided. It’s important to have data close enough to the actual measurement. What is the measurement error if something is supposed to be 2 cm long and we measure it at 2.1 cm?
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Once a product is launched, it’s time to understand its performance. The design is done, and the assembly process is working. Now we can focus on answering the question: is the product hitting reliability targets or not?
Thus, we do field data analysis. [Read more…]
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…]
There are three approaches to setting tolerance limits for design elements. Each has ramifications for the eventual manufacturability and reliability performance—design for manufacturability and reliability hinges on correctly setting design tolerances. [Read more…]
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.
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…]
The survival of an individual item is difficult to determine. Use and environmental conditions, along with material and assembly differences, count. Let’s explore how to model and use the naturally occurring variability to improve reliability performance. [Read more…]
Years ago I was a part of a reliability engineering community and I had not met more than two or three members. This was before the internet and was using a new-fangled system called an email list.
At the time, it filled the role of helping me understand the many facets of reliability engineering. It helped me answer questions and allowed me to help others as well.
My desire is to help create more such communities that can help you and your organization improve the discussion concerning reliability. Let’s explore exactly how to make this work. [Read more…]
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.