Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
The LinkedIn ASQ RRD group published this question from a reliability manager. Replies included:
Are generic lists of failure information useful to FMEA teams? Are they a good idea or not?
There is a lot of discussion amongst FMEA practitioners on automating and standardizing FMEAs. In this article, I will discuss the pros and cons of using generic lists of failure modes, effects and causes.
The Oxford English dictionary defines “generic” as “characteristic of or relating to a class or group of things; not specific.” [Read more…]
Generations of products have similar field reliability functions because they are designed, processed, shipped, sold, and used in similar environments by similar customers. Replacement parts have similar reliability functions depending on replacement number: 1st, 2nd,….
Biostatisticians use David Cox’ proportional hazard (PH) survival function models to quantify effects of treatment or risk factors. Proportional hazard models could describe product’s failure modes, parts’ reliabilities in successive replacements, or products’ reliabilities in successive generations. [Read more…]
As engineering professionals, we’re no strangers to complex problem-solving scenarios that demand rigorous analysis, innovative thinking, and efficient decision-making. Whether it’s designing new components, optimizing processes, or addressing infrastructure challenges, our success hinges on our ability to approach problems with a structured and creative mindset.
One method that can significantly boost our problem-solving prowess is the “Six Hats Method” – a systematic approach that allows us to don different thinking hats, unlocking diverse perspectives for comprehensive and effective solutions. Let’s see how the Six Hats Method can empower engineering professionals to navigate challenges with clarity, focus, and ingenuity.
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Hopefully, you have already seen that utilizing R for engineering can unlock a host of powerful tools and techniques and give you the power of data visualization.
In using R, you will likely work with various objects (data files, scripts, analyses, reports etc.). As with other things, creating structure for how you accomplish things in R can be important for both effectiveness and efficiency.
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In the world of reliability engineering, ensuring the long-term dependability and safety of components is of paramount importance. Nondestructive Testing (NDT) technologies have emerged as indispensable tools for reliability professionals in various industries, including aerospace, automotive, manufacturing, and power generation. By enabling the inspection and evaluation of materials and components without causing damage, NDT techniques play a crucial role in enhancing short and long-term reliability.
In this blog post, we will explore several NDT technologies and how they contribute to improving component reliability.
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If there is one graphical technique that deserves a lot more attention that it gets and that every engineer needs to utilize in their day to day, my vote would definitely go for the mighty Youden Plot.
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The (age-specific or actuarial) force of mortality drives the demand for spares, service parts, and most products. The actuarial demand forecast is Σd(t‑s)*n(s), where d(t-s) is (age-specific) actuarial demand rate and n(s) is the installed base of age s, s=0,1,2,…,t. Ulpian, 220 AD, made actuarial forecasts of pension costs for Roman Legionnaires. (Imagine computing actuarial demand forecasts with Roman numerals.) Actuarial demand rates are functions of reliability. What if reliability changes? We Need Statistical Reliability Control (SRC).
Actuarial demand forecasts require updating as installed base and field reliability data accumulates. Actuarial failure rate function, a(t), is related to reliability function, R(t), by a(t) = (R(t)-R(t-1))/R(t-1), t=1,2,… If products or parts are renewable or repairable, then actuarial demand rate function, d(t), depends on the number of prior renewals or repairs by age t [George, Sept. 2021].
[Last month I mentioned that the next article would be on the subject of the application of models in the FMEA process. I am postponing that important topic, in order to do more research. Stay tuned . . .]
This month, I want to discuss one of the most common problems that FMEA teams face: getting confused about the difference between failure modes, effects and causes.
“Things are not always as they seem; the first appearance deceives many.” Phaedrus
In 1956, Harvard Business Review published a landmark article by economist and business leader Armand Feigenbaum titled “Total Quality Control” that summarized the quality control system he developed during his long tenure at General Electric and gave prominence to many concepts still used in quality management today. One of those concepts was cost of quality measurement.
The goal of a cost of quality measurement system is to provide manufacturing leaders with a tool that can help drive process improvements. By understanding the magnitude and sources of their quality costs, manufacturing executives, managers, engineers, and technicians can more effectively direct their efforts, improvement strategies, and capital budgets toward reducing them.
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What is the difference between FMEA and FMECA? Are they the same or different? The answer may surprise you. Let’s explore this topic.
Today we look at how to draw simple flowcharts in R.
I think I am not far off when I say that flowcharts are an essential tool in the engineering toolbox. They provide a visual way to describe a set of activities, or a a process if you will. This can range from listing sequential steps in a manufacturing process to laying out a project plan to describing a decision making process (think decision trees).
“If you can’t describe what you are doing as a process, you don’t know what you’re doing.”
– W. Edwards Deming
It comes as no surprise that engineers love to use flowcharts to describe or document stuff. If you’re like me, you’ve probably used various Microsoft Office applications to draw flowcharts. I, for example, have mastered creating flowcharts in PowerPoint over the years. Some prefer Visio or maybe some other application (Figma, Miro and the like).
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Do you want easy demand forecasts or do you want to learn and use the reliabilities of service parts and make demand forecasts and distribution estimates, without sample uncertainty? Would you like to do something about service parts’ reliability? Would you like demand forecast distributions so you could set inventory policies to meet fill rate or service level requirements? Without sample uncertainty? Without life data? Don’t believe people who write that it can’t be done!
Today we look at a couple different ways to visualize the distribution of your data.
Understanding the distribution of your data can be useful for engineers undertaking various tasks. The fact of the matter is that there are many different ways in which one can get an idea of the distribution of the data they’re interested in, one of which is density curves.
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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.
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