Dear friends, Hemant Urdhwareshe explains concepts of statistical degrees of freedom and replication in Design of Experiments (DOE). Hope you find this interesting and useful.
[Read more…]Degrees of Freedom and Replication in Design of Experiments
on Tools & Techniques
A listing in reverse chronological order of articles by:
- Dennis Craggs — Big Data Analytics series
- Perry Parendo — Experimental Design for NPD series
- Dev Raheja — Innovative Thinking in Reliability and Durability series
- Oleg Ivanov — Inside and Beyond HALT series
- Carl Carlson — Inside FMEA series
- Steven Wachs — Integral Concepts series
- Shane Turcott — Learning from Failures series
- Larry George — Progress in Field Reliability? series
- Gabor Szabo — R for Engineering series
- Matthew Reid — Reliability Engineering Using Python series
- Kevin Stewart — Reliability Reflections series
- Anne Meixner — Testing 1 2 3 series
- Ray Harkins — The Manufacturing Academy series
SPC Q&A Part 1
Favorite Questions and Answers from my Course “Statistical Process Control (SPC) Using Microsoft Excel”, Pt 1
Teaching manufacturing-related skills online offers a host of intangible benefits such as:
- Interacting with professionals around the world,
- Helping learners take the next step in their careers,
- Witnessing the “a ha” moments of the learners taking my classes, and
- Sharing the knowledge I’ve accumulated (and continue to accumulate) in my career.
And in one specific piece of the teaching process, I can experience all four benefits simultaneously, that is, answering student questions.
In this first article of a three-part series, I will share with you some of my favorite questions and answers from “Statistical Process Control (SPC) Using Microsoft Excel”, a course 7,800+ students from 126 countries have taken over the past since I launched it nearly 7 years ago.
[Read more…]Key Teaching Principle # 10: Application
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.
I have been impressed with the urgency of doing. Knowing is not enough; we must apply. Being willing is not enough; we must do. Leonardo da Vinci
Key Teaching Principle # 10 compels the instructor to ensure that each and every student learns how to apply the material being taught, in a variety of realistic scenarios. [Read more…]
Fractional Factorial Design in Minitab
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!
[Read more…]Design of Experiments: Terms and Definitions
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.
[Read more…]Kaplan-Meier Ignores Cohort Variability!
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].
Histogram and Descriptive Statistics on Excel
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.
[Read more…]Time to Event Analysis: An Introduction
Introduction
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.
Pareto Chart on Excel
Understanding FMEA Compensating Provisions
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.
Reliability Testing Sampling Plans Part-2 (PRST and Fixed Length Plans)
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.
[Read more…]MTBF Correlation vs. Causation: MIL-HDBK-217G
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.
[Read more…]Reliability Sampling Plans Part-1 (Basic Concepts)
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).
[Read more…]The Ubiquitous Normal Distribution
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.
Sample size in Reliability Testing: Part-2
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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