Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
My wife says I am wasting my time trying to change reliability statistics, so I polled the www.linkedin.com Reliability Leadership…, ASQRRD, IEEE Reliability, “Biostatistics, and No MTBF groups. The polls claimed that “Life data, censored or not, is required to estimate MTBF, reliability function, failure rate function, or survivor function. TRUE? FALSE? or DON’T KNOW.” I am grateful for the responses.
[Read more…]Some defense-related applications require a special type of criticality analysis, called Quantitative Criticality Analysis to supplement FMEA applications. This is the “C” in what is called FMECA: Failure Mode, Effects and Criticality Analysis. I’ll shorten Criticality Analysis to CA in this article.
What is Quantitative CA? When and why it is used? Can Quantitative Criticality Analysis be used in commercial applications?
Someone asked, “…if you can give me quick explanation: For Example, EPRD 2014 part, Category: IC, Subcategory: Digital, Subtype1: JK, Failure Rate (FPMH) = 0.083632 per (million) calendar hours! How do you convert that to operational hours?” I.e., time-to-failure T has exponential distribution in calendar (million) hours with MTBF 11.9571 (million) hours.
Did the questioner mean how to convert calendar-hour MTBF into operating-hour MTBF? David Nichols’ article does that for 217Plus MTBF predictions, based on “the percentage of calendar time that the component is in the operating or non-operating (dormant) calendar period, and how many times the component is cycled during that period.” I.e., MTBF/R where R is the proportion of operating hours per calendar hour.
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This is the sixth edition of the R for Engineering newsletter, and today we look at the ultimate prioritization tool – Pareto charts!
Pareto charts are a core tool for anyone who makes decisions, whether it is selecting a project or problem to solve, combing through last year’s spend or deciding on what equipment to purchase this year. The list goes on; bottom line is that Pareto charts simply allow you to focus on what’s important and cut through what may be interesting but unimportant.
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Dear Larry
Thank you for your data request for breast implant data and apologies for the delay in responding. The data available is:
My colleagues have been copied into this email to show your request has been actioned. I hope this is helpful. [Read more…]
In the last edition of R for Engineering, we learned how to draw small multiple plots in R and harness the power of comparison. We went from a busy graph to being able to use ggplot’s faceting functions to create a small multiples plot. If you need a recap, here’s a link to the last edition.
That is to say, nature’s laws are causal; they reveal themselves by comparison and difference, and they operate at every multi-variate space-time point.
– Edward Tufte
Small multiples have many uses in engineering, but the one I personally use them the most for is characterization and diagnosis. In my line of work, which is quality engineering, the ability to diagnose problems in physical systems (both product and machine/process-related) is a critical skill, and I will go as far as to say that diagnosing problems is a critical skill in any engineering discipline.
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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?