Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
Dear friends, this video illustrates how to create and analyze a fractional factorial design using Minitab software with an application example. You can watch our other video on basic concepts in Fractional Factorial Designs: DOE-5: Fractional Factorial Designs, Confounding and Resolution Codes. You can watch all our videos on DOE by clicking here to see the playlist: DOE-2: Application of Design of Experiments. We hope you are finding our videos useful!
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Dear friends, many of you have requested for more videos about Design of Experiments. In this video, Hemant Urdhwareshe explains basic terms and definitions in DOE. These include OFAT vs DOE, Types of Factors, Levels, treatments, steps in DOE, nuisance or noise factors, blocking, randomization, covariates, etc. Hope you find this video useful to understand these basic concepts.
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The Kaplan-Meier reliability estimator is the nonparametric, maximum likelihood estimator from right-censored, grouped lifetime data. It has been used since publication, most statistics programs do it, and it has been taught since I was in school. I give away a spreadsheet version.
Lifetime data requires tracking individual subjects or units from their start to failure, death, or censoring. Data may be collected periodically grouped by cohorts: monthly sales, ships, or other collections of individuals, subjects, or units and each cohort’s lifetimes. Data could be displayed in a “Nevada” table with random cohorts in one column, and each cohort’s lifetimes grouped in periodic age-at-failure intervals in columns to the right [Schenkelberg].
Dear friends, some of the participants in our training programs requested me to make a video on how to use Excel to plot histograms and descriptive statistics using Analysis ToolPak. This video illustrates how to do this for a sample data of admit time of patients in a hospital. Hope you find this useful.
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I am currently reading the book Survival Analysis: Techniques for Censored and Truncated Data, Second Edition (John P. Klein and Melvin L. Moescheberger). Although the techniques presented in this book focus on applications in biology and medicine, the same statistical tools can also be applied to disciplines ranging from engineering to economics and demography. I have a background in mechanical engineering and am interested in applying survival modeling concepts to data from reliability engineering, manufacturing and quality assurance. This article is the first of, hopefully, many articles that I intend to write as I finish reading different chapters from the book.
The data set(s) that will be analysed are the ones that have been used as examples in another book: Statistical Methods for Reliability Data, Second Edition (William Q. Meeker, Luis A. Escobar, Francis G. Pascual). Both the books I mentioned are excellent resources for anyone who is interested in learning more about this topic.
In this article, we will analyze vehicle shock absorber failure time data Failure time data is also known as survival data, life data, event-time data or reliability data, depending on the field of study. and estimate a few basic survival quantities. The data contains failure times (in kilometers driven) and the mode of failure, first reported by O’Connor (1985) O’Connor, P. D. T. (1985). Practical Reliability Engineering. Wiley. [54, 610]. We will ignore the mode of failure for now and will only consider whether a failure occurred or not, i.e., censored. In a future article, I plan to use the different failure modes to discuss competing risks for time-to-failure data.
Risk is a function of how poorly a strategy will perform if the “wrong” scenario occurs. Michael Porter
The use of Compensating Provisions in FMEA is a key part of many FMEA standards. Regardless of what FMEA standard you are using, everyone who aspires to doing FMEAs properly should understand the role of mitigating the risk of very high severity.
Dear friends, Institute of Quality and Reliability is happy to release this video on Reliability Testing Sampling Plans. In this is Part-2 of the video, Hemant Urdhwareshe has explained the Probability Ratio Sequential Test (PRST) and Fixed Length plans from MIL-Handbook-781. These include illustrated explanation of the plans and applicability.
We are sure, viewers will find this video useful!
Earlier, we released part 1 of the video, in which Hemant explained the concepts of sampling risks and operating characteristics (OC) curves.
We also suggest viewers to see our related videos on Hazard Rate and related concepts for better understanding of this video.
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People claim poor correlation of predicted and observed MTBFs. That is understandable because handbook failure rates and fudge factors for quality and environment were derived from unknown populations or samples. People also claim there is no basis for applying statistics or probability to MTBF predictions. MTBF predictions use failure rate averages that lack statistical causation. Why not incorporate Paretos in MTBF predictions?
Paretos are fractions of equipment failures caused by each type of part or subsystem. They represent what really happens. Incorporating Paretos requires statistics to adjust MTBF predictions. That causes Paretos in MTBF predictions to match field Paretos. A 1992 ASQ Reliability Review article “MIL-HDBK-217G” proposed using observed Paretos to adjust handbook MTBF predictions with a “Reality” factor.
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Dear friends, Institute of Quality and Reliability is happy to release this video on Reliability Sampling Plans. In this is Part-1 of the video, Hemant Urdhwareshe has explained the basic concepts in Sampling plans. These include Sampling Risks and Operating Characteristics. We are sure, viewers will find this video useful!
We will release part-2 of the video where Hemant will explain Fixed Length Reliability Test Plans and Sequential Test Plans (PRST).
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Underpinning the coherence of statistical process control, process capability analysis and numerous other statistical applications is a phenomenon found throughout nature, the social sciences, athletics, academics and more. That is, the normal distribution, or less formally, the bell curve. Because of its ubiquity, this normal distribution is arguably the most important data model analysts, engineers, or quality professionals will learn.
This is my second video on Sample Size in Reliability Testing! In this video, we will explain the Weibayes Approach to estimate sample size and estimating test length when sample size and shape parameter is known.
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Insert these pages into your copy of MIL-HDBK-217. The boldface text is changed to MIL-HDBK-217E [1], section 5.2, on parts count reliability prediction. The changes explain how to use “Paretos,” proportions of parts failing in the field, to compute a reality factor that makes predicted Paretos match field Paretos. You can use field Paretos to calibrate predictions for new equipment. You probably have field Paretos on related parts used in your other equipment, which is now in the field. Remember, the field determines reliability.
[Read more…]Dear friends, I am happy to release this video about determining sample size in reliability and functional testing! The video discusses determining sample size with Success Run Theorem (or Success Testing) will zero failures as well as given number of failures. I have illustrated use of basic formula and calculation as well as use of various templates to determine sample size. Hope you find this important and interesting!
This is part-1 of my videos on sample size! We recommend viewers to watch our video on Binomial Distribution before watching this video, in case they have not seen it before
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Hard work alone doesn’t guarantee success in business. Even after you’ve invested your inspiration, money, emotions, creativity, and prayers, the reality is that we live in a highly competitive world. You can’t afford to simply tread water. So, let’s assume you’ve either made a strong start in your field or joined a profitable company. What ensures that your future will be better than today? The answer lies in leadership and the team deciding to continually evolve, change, and improve.
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