Reliability Analysis With Minitab

Statistical Analysis for the Reliability Engineering ProfessionalEffectively conduct reliability analysis using the world's leading statistical software. Reliability Analysis with Minitab outlines statistical concepts and applications, explains the theory of probability, reliability analysis, and quality improvement, and provides step-by-step instr

Many reliability engineers are gainfully employed in considerations of the physical nature of components and systems-bringing to bear theories and methodologies of physics, electronics, mechanics, material science, chemistry, and so on. But when a product has been designed and manufactured, its performance in terms of durability, strength, and life become a matter of test, measurement, and analysis. Statistical theories and methodologies provide a large number of analytical tools to assist the reliability engineer in studying the performance of products and the fruits of the physical considerations, even revealing further improvements that can be made in the physical properties. Hence, reliability is a multidisciplined field of endeavor. Statistical theories and methodologies allow estimation of important characteristics as well as levels of confidence or assurance (or lack thereof) with respect to the estimations. They also provide direction in actions necessary to improve estimates and confidence levels if results are too variable to render important decisions. Some derivations are contained in this text, but the approach here is meant to be more practical, in following each topic introduced and expanded with examples. On each topic covered, reasonably practical examples are used to illustrate and demonstrate the procedures introduced and discussed. For all of these examples either Excel files or Minitab files or both have been prepared (available from Quality Press). They can be readily accessed and opened directly in their respective software packages to permit the preparation of new files specifically for use by the reader. "This book provides a much-needed theoretical text to aid advanced reliability engineering data analysis. Applications using Excel and Minitab support a broad span of probability applications for reliability data analysts. I most strongly recommend this book for seasoned Six Sigma Black Belts or statisticians who must support Design for Six Sigma applications for new product development projects. It's rich in food for thought as well as providing a most nourishing banquet for consumption by engineers --- it is not for light reading as a snack, but it must be consumed as a seven-course meal!" Gregory H. Watson Chairman, International Academy for Quality ASQ Past-President and Fellow

Complete overview of the theory and fundamentals of Reliability Analysis applied with Minitab and Python tools Reliability Analysis Using Minitab and Python expertly applies Minitab and Python programs to the field of reliability engineering, presenting basic concepts and explaining step-by-step how to implement statistical distributions and reliability analysis methods using the two programming languages. The textbook enables readers to effectively use software to efficiently process massive amounts of data while also reducing human error. Examples and case studies as well as exercise and questions are included throughout to enable a smooth learning experience. Excel files containing the sample data and Minitab and Python example files are also provided. Students who have basic knowledge of probability and statistics will find this textbook highly approachable. Nonetheless, it also covers material on basic statistics at the beginning, so students who are not familiar with statistics can follow the material as well. Written by a highly qualified author in the field, sample topics covered in Reliability Analysis Using Minitab and Python include: Establishing a basic statistical background, with a focus on probability, joint probability, union probability, conditional probability, mutually exclusive events, and bayes’ rule Statistical distributions, with a focus on discrete cases, continuous cases, exponential distribution, Weibull distribution, normal distribution, and lognormal distribution Reliability data plotting, with a focus on straight line properties, least squares fit, linear rectification, exact failure times, and readout failure data Accelerated life testing, with a focus on accelerated testing theory, exponential distribution acceleration, and Weibull distribution acceleration System failure modeling, with a focus on reliability block diagram, series syste model, parallel system model, k-out-of-n system model, and minimal paths and minimal cuts. Repairable systems, with a focus on corrective and preventive maintenances, availability, maintainability, and preventive maintenance scheduling. Reliability Analysis Using Minitab and Python serves as an excellent introductory level textbook on the topic for both undergraduate and graduate students. It presents information clearly and concisely and includes many helpful additional learning resources to aid in understanding of concepts, information retention, and practical application.

Reliability Analysis Using MINITAB and Python Complete overview of the theory and fundamentals of Reliability Analysis applied with Minitab and Python tools Reliability Analysis Using Minitab and Python expertly applies Minitab and Python programs to the field of reliability engineering, presenting basic concepts and explaining step-by-step how to implement statistical distributions and reliability analysis methods using the two programming languages. The textbook enables readers to effectively use software to efficiently process massive amounts of data while also reducing human error. Examples and case studies as well as exercises and questions are included throughout to enable a smooth learning experience. Excel files containing the sample data and Minitab and Python example files are also provided. Students who have basic knowledge of probability and statistics will find this textbook highly approachable. Nonetheless, it also covers material on basic statistics at the beginning, so students who are not familiar with statistics can follow the material as well. Written by a highly qualified author in the field, sample topics covered in Reliability Analysis Using Minitab and Python include: Establishing a basic statistical background, with a focus on probability, joint probability, union probability, conditional probability, mutually exclusive events, and bayes’ rule Statistical distributions, with a focus on discrete cases, continuous cases, exponential distribution, Weibull distribution, normal distribution, and lognormal distribution Reliability data plotting, with a focus on straight line properties, least squares fit, linear rectification, exact failure times, and readout failure data Accelerated life testing, with a focus on accelerated testing theory, exponential distribution acceleration, and Weibull distribution acceleration System failure modeling, with a focus on reliability block diagram, series system model, parallel system model, k-out-of-n system model, and minimal paths and minimal cuts. Repairable systems, with a focus on corrective and preventive maintenances, availability, maintainability, and preventive maintenance scheduling Reliability Analysis Using Minitab and Python serves as an excellent introductory level textbook on the topic for both undergraduate and graduate students. It presents information clearly and concisely and includes many helpful additional learning resources to aid in understanding of concepts, information retention, and practical application.

As the Lead Reliability Engineer for Ford Motor Company, Guangbin Yang is involved with all aspects of the design and production of complex automotive systems. Focusing on real-world problems and solutions, Life Cycle Reliability Engineering covers the gamut of the techniques used for reliability assurance throughout a product's life cycle. Yang pulls real-world examples from his work and other industries to explain the methods of robust design (designing reliability into a product or system ahead of time), statistical and real product testing, software testing, and ultimately verification and warranting of the final product's reliability

Need to learn Minitab? Problem Solved! Get started using Minitab right way with help from this hands-on guide. Minitab Demystified walks you through essential Minitab features and shows you how to apply them to solve statistical analysis problems. Featuring coverage of Minitab 16, this practical guide explores the Minitab interface and the full range of Minitab graphics, Distribution models, statistical intervals, hypothesis testing, and sample size calculations are clearly explained. The book covers modeling tools of regression and the design of experiments (DOE) as well as the industrial quality tools of measurement systems analysis, control charts, capability analysis, acceptance sampling, and reliability analysis. Detailed examples and concise explanations make it easy to understand the material, and end-of-chapter quizzes and a final exam help reinforce key concepts. It's a no-brainer! You'll learn about: Accessing powerful Minitab functions with the Minitab assistant Confidence, prediction, and tolerance intervals Designing and analyzing experiments with hard-to-change variables Statistical process control (SPC), Six Sigma applications, and quality control Predicting the economic impact of sampling Analyzing life data with additional variables Simple enough for a beginner, challenging enough for an advanced student, and thorough enough for a Six Sigma professional, Minitab Demystified is your shortcut to statistical analysis success!

Since the publication of the second edition of Applied Reliability in 1995, the ready availability of inexpensive, powerful statistical software has changed the way statisticians and engineers look at and analyze all kinds of data. Problems in reliability that were once difficult and time consuming even for experts can now be solved with a few well-chosen clicks of a mouse. However, software documentation has had difficulty keeping up with the enhanced functionality added to new releases, especially in specialized areas such as reliability analysis. Using analysis capabilities in spreadsheet software and two well-maintained, supported, and frequently updated, popular software packages—Minitab and SAS JMP—the third edition of Applied Reliability is an easy-to-use guide to basic descriptive statistics, reliability concepts, and the properties of lifetime distributions such as the exponential, Weibull, and lognormal. The material covers reliability data plotting, acceleration models, life test data analysis, systems models, and much more. The third edition includes a new chapter on Bayesian reliability analysis and expanded, updated coverage of repairable system modeling. Taking a practical and example-oriented approach to reliability analysis, this book provides detailed illustrations of software implementation throughout and more than 150 worked-out examples done with JMP, Minitab, and several spreadsheet programs. In addition, there are nearly 300 figures, hundreds of exercises, and additional problems at the end of each chapter, and new material throughout. Software and other files are available for download online

Industrial Statistics with MINITAB demonstrates the use of MINITAB as a tool for performing statistical analysis in an industrial context. This book covers introductory industrial statistics, exploring the most commonly used techniques alongside those that serve to give an overview of more complex issues. A plethora of examples in MINITAB are featured along with case studies for each of the statistical techniques presented. Industrial Statistics with MINITAB: Provides comprehensive coverage of user-friendly practical guidance to the essential statistical methods applied in industry. Explores statistical techniques and how they can be used effectively with the help of MINITAB 16. Contains extensive illustrative examples and case studies throughout and assumes no previous statistical knowledge. Emphasises data graphics and visualization, and the most used industrial statistical tools, such as Statistical Process Control and Design of Experiments. Is supported by an accompanying website featuring case studies and the corresponding datasets. Six Sigma Green Belts and Black Belts will find explanations and examples of the most relevant techniques in DMAIC projects. The book can also be used as quick reference enabling the reader to be confident enough to explore other MINITAB capabilities.