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Enhancing Component Reliability with Nondestructive Testing Technologies

Enhancing Component Reliability with Nondestructive Testing Technologies

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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Statistical Reliability Control?

Statistical Reliability Control?

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].

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Common FMEA Confusion

Common FMEA Confusion

[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

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Cost of Quality Measurement and Reporting

Cost of Quality Measurement and Reporting

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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Being in a State of Flow(charting)

Being in a State of Flow(charting)

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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Time Series Forecasts for Service Parts?

Time Series Forecasts for Service Parts?

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!

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Density Curves (With a Reliability Engineering Example)

Density Curves (With a Reliability Engineering Example)

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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Poll: “Is life data required…?”

Poll: “Is life data required…?”

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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Application of Quantitative Criticality Analysis in FMEA

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?

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Convert a Constant Failure Rate to Operating Hours

Convert a Constant Failure Rate to Operating Hours

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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Priorities, priorities…

Priorities, priorities…

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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Reliability of Breast Implants

Reliability of Breast Implants

Dear Larry

Thank you for your data request for breast implant data and apologies for the delay in responding. The data available is:

  • The number of women receiving implants, by year, by major manufacturer
  • Number of Explants: All Manufacturers (inc. Others and Unknown Brands)

My colleagues have been copied into this email to show your request has been actioned. I hope this is helpful. [Read more…]

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Small Multiples for Characterization

Small Multiples for Characterization

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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