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Life Data Analysis of Right Censored data using Minitab Software

Life Data Analysis of Right Censored data using Minitab Software

I am happy to share my next video on ‘Life Data Analysis of Right Censored Data using Minitab Software’ as many viewers had requested! In this video, we revisit the types of failure data and explain procedure to analyse right censored data in Minitab software with an example.

The procedure is predominantly in three steps:

  • 1. Identify the distribution that best fits our data
  • 2. Estimate parameters of the selected distribution
  • 3. Estimate reliability or probability of failure at specified time(s)

The procedure is explained in detail using an application example of camshaft failure data. In the video, I have also explained how to estimate expected number of failures by 100000 Kilometres and also the 95% upper bound which is the worst-case scenario. I am sure, you will find this video interesting and useful for practical application of Life Data Analysis! Your feedback on the video is welcome!

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Why Use Nonparametric Reliability Statistics?

Why Use Nonparametric Reliability Statistics?

Fred asked me to explain why use nonparametric statistics? The answer is reality. Reality trumps opinion, mathematical convenience, and tradition. Reality is more interesting, but quantifying reality takes work, especially if you track lifetimes. Using field reliability reality provides credibility and could reduce uncertainty due to tradition and unwarranted, unverified assumptions.

Data is inherently nonparametric. Cardinal numbers are used for period counts: cohorts, cases, failures, etc. Accounting data is numerical; it is derived from data or from dollars required by GAAP (Generally Accepted Accounting Principles); e.g., revenue = price*(products sold), service cost = (Cost per service)*(Number of services), or numbers of spare parts sold. Why not do nonparametric reliability estimation, with or without lifetime data?

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Statistical Tolerancing using Monte Carlo Simulation

Statistical Tolerancing using Monte Carlo Simulation

One of the weak areas in designing parts is deciding tolerances of various parts. Most engineers are familiar with Arithmetic Tolerance stack up analysis wherein they check impact of maximum and minimum values of various tolerances on assembly of parts. However, this can often result in high manufacturing cost. Thus, it may be more appropriate to analyse tolerances using statistical tolerance stack up approach. This can be performed using Monte Carlo Simulation. In one of the previous videos, I had shown how to predict reliability using Monte Carlo simulation. In this video, I will explain how to perform statistical tolerance stack up analysis using Monte Carlo Simulation. I have used Simular software to demonstrate this with a practical example. Simular is a free software (emailware) which can be downloaded from https://www.simularsoft.com.ar/. However, one can use other software such as Crystal Ball, @Risk etc. for such analysis. Statistical tolerance stack up is usually an integral part of Design for Six Sigma (DFSS) projects. I hope viewers will find this video useful. Feedback is welcome!

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Reliability Prediction using Monte Carlo Simulation

Reliability Prediction using Monte Carlo Simulation

In the last video on stress-strength interference, we have seen the analytical method. This has limitations and often cannot be used in real life problems in reliability prediction. For example, velocity of windmill may have Weibull or lognormal distribution, elevators may have particular application load cycles which can only be modelled using empirical distributions. In such situations, we need to use Monte Carlo Simulation using various other distributions. I will discuss and explain this technique in this video.

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Key Teaching Principle #3: Managing Attention

As covered in the first article in this series, Principles of Effective Teaching, reliability engineers, FMEA team leaders, and other quality and reliability professionals are often called upon to teach the principles of reliability or FMEA. Whether you are a student who wants to enhance your learning experience, an instructor who wants to improve teaching results, or an engineer who wishes to convey knowledge to another person, this series will offer practical knowledge and advice.

The Importance of Managing Attention

“Attention is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought.”  William James

Whether you are conveying knowledge to one person or teaching a class, there is no more important factor than getting and maintaining the attention of the other person. Managing attention is a skill that can be learned.

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Reliability Prediction using Stress Strength Interference (Analytical Method)

Reliability Prediction using Stress Strength Interference (Analytical Method)

Often, products fail, and we don’t understand why! One of the reasons why such failures occur is not giving consideration for variation in load or stress levels. For example, potholes and speed breakers can create excessive stress on automobile suspensions; or larger number of clothes washed in a machine more often than the designer has considered; or a bus used in public transport would carry varying number of passengers! Designers often do not realize that the materials used in the product will also have variations! For example, wall thicknesses of castings will vary; or chemical composition of steel will vary from lot to lot! Thus, designers need to address the variations in usage patterns and variations in the materials used in the product as these can seriously affect reliability of systems! In my recently uploaded video, I have discussed how to predict reliability when load (stress) and strength of the part both vary and can be modelled using normal distribution. The video will also be very useful to those who wish to take ASQ Certified Reliability Engineer exam and all those who wish to learn the basics of reliability in design. This is the first video where the stress and strength can be modelled using normal distribution. In the next video related to this topic, I will explain reliability prediction using Monte Carlo Simulation.

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The Seven Basic Quality Tools: Getting Started with Data Visualizations

The Seven Basic Quality Tools: Getting Started with Data Visualizations

A common first step in the learning journey of a quality engineer is mastering a set of data visualization tools called the “Seven Basic Quality Tools.” Popularized by an architect of the modern quality movement, Karou Ishikawa, the seven basic quality tools allow practitioners to readily extract useful insights from raw data and build a foundation for further statistical analysis. These tools stand as pillars of systematic problem-solving, aiding in the identification, analysis, and improvement of processes.

These seven tools, in order of complexity, are: Check Sheets, Flowcharts, Cause and Effect Diagrams, Histograms, Pareto Diagrams, Scatter Plots, and Control Charts.

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Weibull Probability Plotting of Complete Data

Weibull Probability Plotting of Complete Data

This video explains step-by-step procedure for probability plotting of failure data. Probability plotting is a technique used to determine whether given data of failures follows a distribution. The video also explains various types of failure data: complete, right censored, left censored and interval censored data. Viewers can try Weibull Probability Plotting to estimate shape parameter beta and scale parameter Eta. The video also shows how to estimate median rank for complete data.

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Time Series vs. Actuarial Forecasts?

Time Series vs. Actuarial Forecasts?

Time series forecasts are easy to make and data are available. They’re like driving while looking in the rear-view mirror. A survey listed 31 forecasting software programs: none actuarial [Yurkewicz]. Actuarial failure forecasts are less biased and are more precise than time series failure forecasts, because actuarial failure forecasts use age-specific failure rates. How much better? 

The example in this article shows the 5% to 95% time series confidence interval width is 44.78 vs. the nonparametric actuarial Kaplan-Meier actuarial forecast width of 12.63, from grouped failure data, and actuarial forecast width of 15.45, from ships and returns counts.

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Life Data Analysis of Complete Data Using Minitab

Life Data Analysis of Complete Data Using Minitab

In this video, Hemant Urdhwareshe discusses how to analyse complete data of failures to identify distribution, identify distribution parameters, estimate reliability at a specified time using Minitab (version 19) software. The file used in the video can be downloaded here: Reliability Data.xls.zip.

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Correlation and Regression on Casio fx991-MS Calculator and Excel

Correlation and Regression on Casio fx991-MS Calculator and Excel

Dear Quality and Reliability Professionals, this video is created to understand and learn how to use Casio fx991-MS calculator for performing correlation and regression with a simple application example that viewers can try. The video will be useful for all those who want to prepare for exams by American Society for Quality (ASQ).

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Key Teaching Principle #2: Connection

  As covered in the first article in this series, Principles of Effective Teaching, reliability engineers, FMEA team leaders, and other quality and reliability professionals are often called upon to teach the principles of reliability or FMEA. Whether you are a student who wants to enhance your learning experience, an instructor who wants to improve teaching results, or an engineer who wishes to convey knowledge to another person, this series will offer practical knowledge and advice.

The Role of Personal Connection When Teaching

Invisible threads are the strongest ties   Friedrich Nietzsche

Key Teaching Principle # 2 is the instructor maintains a genuine connection with each of the students.

Our scientific knowledge has accelerated so rapidly that we sometimes forget the importance of human connection to our well being.

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Convert AFRs to Field Reliability?

Convert AFRs to Field Reliability?

AFRs are periodic ratios of failure counts divided by installed base. Have you seen meeting rooms wallpapered with AFR charts (Annualized Failure Rate)? Have you sat through debates about the wiggles in AFR charts? Fred Schenkelberg wondered if reliability could be estimated from AFRs and their input data? How about age-specific reliability and actuarial failure rate functions? Actuarial forecasts? MTBFs? Wonder no more!

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