Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
Did you know that early FMEA standards did not include recommendations to reduce risk? They limited the analysis to the technical risk, without making specific recommendations. The first time I am aware of that an FMEA standard added a column called “Recommended Actions” was in 1993. Thankfully, it is common practice today to include Recommended Actions in FMEAs.
But what makes for excellent Recommended Actions and what is their role in an FMEA? We’ll begin with the fundamentals.
In this week’s edition, I introduce you to the concept of small multiples, and, more importantly, how to make them in R. This is one of those really low effort-super high return kind of features of R that can make you look like a rock star of data visualization. So, without further ado, let’s jump right into it!
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In the early 1700’s, English mathematician and Presbyterian minister Thomas Bayes derived the eponymous mathematical theorem that allows us to calculate the probability of an event occurring based on prior knowledge of conditions that might be related to the event.
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In this week’s edition, we dig into a scenario you’ve probably run across when working in Excel or other software, for example Minitab —at least I have, many times.
Say you have a complete dataset. The data has been collected, and you’re now getting ready to run plot it or run some sort of analysis on it. It should be plug and play, but it ends up not being the case as the data is not formatted in the right way, and you’re not able to run your analysis (it happens pretty frequently if you ask me).
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If this is your first time reading my newsletter: I am thrilled that you decided to give it try!
If this is not your first time: I’m glad you’re still here!
We’ve got a few things to go through in this week’s edition.
However, before we get into the cool stuff, that is showcasing useful functionality and interesting use cases, I feel it would behoove me to lay down some of the foundational things you’ll need to do to get you started in R, should you be interested.
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With U.S. annualized inflation rates exceeding 5.0% each month since May of 20211 – the longest stretch this century – the need for sustainable cost improvements has rarely been greater. And one source of cost reductions available to nearly every manufacturer is the elimination of waste and quality defects within their own facilities. Understanding the major continuous improvement (CI) strategies may help manufacturing leaders find a path toward lowering their costs and creating healthier margins for their organizations.
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Email from www.smartcorp.com advertised how to forecast inventory requirements using time-series analyses: single and double exponential smoothing, linear and simple moving average, and Winters models. SmartCorp compares alternative times-series forecasts in a “tournament” that picks the best forecast. Charles Smart says forecasting, “…particularly for low-demand items like service and spare parts — is especially difficult to predict with any accuracy.”
Time series forecasts also quantify variance. Excel’s time-series FORECAST() functions do exponential smoothing, account for seasonality and trend, and “pointwise” confidence intervals. Pointwise means only one confidence interval is valid at a time; not a confidence band on several forecasts!
It all started two years ago. I had been in engineering, mostly in quality engineering, all my career, and at that point decided I would try and expand my analytical capabilities as an engineer. Not that I didn’t already have tools at my fingertips; I would use Excel, a lot. I was actually pretty good at it, having developed even custom applications with macros and all the bells and whistles. I had Minitab, which most engineers in my line of work also use. If it’s not Mintiab, then it is JMP or one of those statistical applications. They’re all fine.
[Read more…]Every FMEA team needs to prioritize risk as part of the procedure. Why? Because companies or organizations have limited resources that must be focused on highest risk. The question becomes, by what method should we prioritize the risk identified in an FMEA?
One of the most commonly asked questions about quality engineering is “What is the difference between quality assurance and quality control?”
What are the covariances of Kaplan-Meier reliability estimates at different ages? I need them for the variance of actuarial demand forecasts and for confidence bands on reliability. I thought cohort reliability estimate variances and covariances in the previous article were a good idea. How good? Not as good as bootstrap and jackknife resampling alternatives!
The Kaplan-Meier reliability function estimator uses right-censored and grouped time-to-failure counts in periodic cohorts (rows in table 1). The Nelson-Aalen cumulative failure rate function estimators are theoretically independent [Aalen, Nelson], but not for some examples. The Kaplan-Meier reliability and actuarial failure rate function estimates at different ages are dependent, so their covariances matter to actuarial forecasts and confidence bands on reliability.
[Read more…]Among students beginning their examination of reliability engineering, one question pops up repeatedly: Where did all these reliability life models come from? On one hand, reliability engineering is deeply entrenched in statistical models … Weibull, exponential, etc. But these models alone, do not fully explain the product life models. There is still a missing piece: the Physics of Failure (PoF).
PoF and reliability models are closely connected concepts, as they both relate to the ability of products, processes, and systems to perform their intended function consistently over time. [Read more…]
One of the more challenging aspects of FMEA is the subject of Occurrence. Part of every FMEA standard or procedure is the assessment of Occurrence based on an agreed-upon scale. Here are some pointers for when and how to use occurrence in an FMEA.
We’ll start with a definition. The Oxford English dictionary defines “occurrence” as “the fact or frequency of something happening.”
The well-known variance of the Kaplan-Meier reliability function estimator [Greenwoood, Wikipedia] can drastically under-or over-estimate variance. The covariances of the Kaplan-Meier reliability pairs at different ages are ignored or neglected. Variance errors and covariance neglect bias the variance of actuarial demand forecasts. Imagine what errors and neglect do to confidence bands on reliability functions.
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Having worked in manufacturing quality for the great majority of my career, with a few tentacles into the field of reliability, I’ve considered many comparisons between the two fields, with of course, my unconscious biases favoring quality. One interesting comparison, for instance, is between job postings for similar positions in these related fields.
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