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Reliability Estimation Efficiency? Without Lifetime Data?

Reliability Estimation Efficiency? Without Lifetime Data?

What is the efficiency of your reliability estimators and failure forecasts? Compare naïve forecasts: guess, AFR, MTBF, or times series extrapolations such as ARIMA, GMDH, and AI, vs. forecasts based on reliability estimators. With grouped lifetime data, the Kaplan-Meier estimator is statistically efficient, under a condition often ignored! With lifetime data you could use maximum likelihood estimators like Weibull or Kaplan-Meier from (a sample of?) grouped lifetimes. Without lifetime data you could estimate reliability from population ships and returns counts from data required by GAAP! This article compares efficiency and cost of forecasts based on alternative data and estimators.

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Reliability Estimation with Kaplan-Meier Nonparametric Method

Reliability Estimation with Kaplan-Meier Nonparametric Method

Hello Friends! I am happy to release this video on Kaplan-Meier (KM) nonparametric method to estimate reliability. The KM method is used in situations when the data cannot be modelled using mathematical distributions such as Weibull, Lognormal etc. The KM method is popularly used in medical science to estimate survival probabilities of patients with diseases such as cancer, kidney diseases, etc.

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Covid-19 RT-PCR Test, Confusion Matrix and International Travel

Covid-19 RT-PCR Test, Confusion Matrix and International Travel

Dear friends, This is Hemant Urdhwareshe! I am uploading this video, which is different from our other videos! In this video, I have explained the “Confusion Matrix” and related terms such as Sensitivity, Specificity, Accuracy with reference to RT-PCR Test to detect Covid-19 infection. I and my son were isolated for Covid-19 as I was detected Covid-19 positive before my return travel and then isolated for about a fortnight in Egypt! This video is a result of my experience and review of travel rules! I am sure you will find this interesting and thought-provoking!

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FMEA Quality Objective 2: SCOPE

“If you don’t know where you are going, you’ll end up someplace else.” – Yogi Berra

As summarized in the first article in this series, “New Series – The Quality of FMEAs,” each month, I will write an article focusing on one of the 14 FMEA Quality Objectives. The intent is to provide insights and application tips for evaluating how well an FMEA meets the quality objectives. By learning to apply FMEA quality objectives, you can help to ensure that product designs and manufacturing processes are safe and reliable. If you didn’t read the introductory article, now is a good time to read it, since it explains FMEA Quality Objectives, and how they are used to create high-quality FMEAs.

In this article, I will outline how to evaluate an individual FMEA against the FMEA Quality Objective for Scope. [Read more…]

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The Answer That’s Clear, Simple, and Wrong

The Answer That’s Clear, Simple, and Wrong

H.L. Mencken, the sharp-witted satirist and critic of early 20th-century American life, once wrote, “For every complex problem, there is an answer that is clear, simple, and wrong.”1 Mencken, born in Baltimore in 1880, was known for his incisive critiques of societal norms and his skepticism of simplistic solutions to complex issues. 

Mencken’s wisdom applies to both everyday life and technical fields. His insights are particularly relevant in the engineering, manufacturing, and reliability disciplines, where the temptation to seek easy answers can lead to costly errors. We’ll start with a relatable everyday example before exploring documented cases in engineering and manufacturing that demonstrate the pitfalls of oversimplified solutions.

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Confidence Intervals (Part 3) for Proportion and Standard Deviation

Dear friends, We are happy to release this third video in our series on Confidence Intervals! In this video, Hemant Urdhwareshe has explained how to determine confidence intervals for population proportion and standard deviation with day-today application examples! Hemant has also illustrated use of templates for quick calculation!

Links to other related videos:

Confidence Interval (Part-1) for Mean (Sigma Known)

Confidence Interval (Part-2) for Mean (Sigma unknown)

Chi-square Distribution

Chi-square Test of One Variance

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Confidence Intervals for Mean ( part-2:) : Using t-distribution

Dear friends, we are happy to release this Part-2 of our videos on Confidence Intervals. In this video, Hemant Urdhwareshe explains how to estimate confidence interval of population mean when population standard deviation sigma is not known In such cases, we need to use Student’s t-distribution instead of normal distribution and Z-Score.

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FMEA Quality Objective 1: FMEA PLAN

Good fortune is what happens when opportunity meets with planning   Thomas Edison

As summarized in the first article in this series, “New Series – The Quality of FMEAs,” each month, I will write an article focusing on one of the 14 FMEA Quality Objectives. The intent is to provide insights and application tips for evaluating how well an FMEA meets the quality objectives. By learning to apply FMEA quality objectives, you can help to ensure that product designs and manufacturing processes are safe and reliable. If you didn’t read the introductory article, now is a good time to read it, since it explains FMEA Quality Objectives, and how they are used to create high-quality FMEAs.

In this article, I will outline how to evaluate an FMEA Plan against the FMEA Quality Objective for FMEA Plans.

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The 1.5 Sigma Shift: What It Is and Why It Matters

One of the most persistent points of confusion in quality engineering is the difference between traditional statistical process capability analysis and the Six Sigma approach. Specifically, why does Six Sigma define a “six sigma” process as having 3.4 defective parts per million (DPPM), when a straightforward application of statistical tables suggests that six standard deviations from the mean should correspond to a far lower defect rate—about 2 parts per billion? The answer lies in what Six Sigma practitioners call the 1.5 sigma shift.

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Remember this Memoryless Exponential Distribution!

Remember this Memoryless Exponential Distribution!

Dear friends, I am happy to release our 59th video on Exponential Distribution! Exponential Distribution is an appropriate mathematical model when failure or hazard rate is constant. It is often known as “Memoryless” as reliability in any time interval is same! Exponential Distribution has many applications in Reliability Engineering such as electronic components. Many reliability procedures in MIL Standards assume constant failure rate and Exponential Distribution! Hope you find the video interesting and useful! Your feedback is welcome! Do subscribe to our channel to get updates on our new videos and programs!

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Accelerated Life Testing (ALT) Part-4 Determination of Activation Energy for Arrhenius Model

Accelerated Life Testing (ALT) Part-4 Determination of Activation Energy for Arrhenius Model

Dear All, This is our fourth video on Accelerated Life Testing (ALT). Some of the viewers had requested for procedure to determine the Activation Energy used in Arrhenius Model. This video explains experimental procedure to determine Activation Energy. Hope you find this useful! If you like the video, click on the “Thumbs-up icon” and subscribe to our channel for more videos on Quality and Reliability!

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