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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Reliability Analysis Using MINITAB and Python
Jaejin HwangNorthern Illinois University, USA
This edition first published 2023
© 2023 John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Library of Congress Cataloging-in-Publication Data
Names: Hwang, Jaejin, author. Title: Reliability analysis using MINITAB and Python / Jaejin Hwang. Description: Hoboken, New Jersey : John Wiley & Sons, 2023. | Includes bibliographical references and index. Identifiers: LCCN 2022029036 (print) | LCCN 2022029037 (ebook) | ISBN 9781119870760 (hardback) | ISBN 9781119870777 (pdf) | ISBN 9781119870784 (epub) Subjects: LCSH: Minitab. | Reliability (Engineering) | Python (Computer program language) Classification: LCC TA169 .H93 2023 (print) | LCC TA169 (ebook) | DDC 620/.00452--dc23/eng/20220906 LC record available at https://lccn.loc.gov/2022029036LC ebook record available at https://lccn.loc.gov/2022029037
Cover image: © whiteMocca/Shutterstock
Cover design by Wiley
Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd, Pondicherry, India
Contents
Cover
Title page
Copyright
About the Author
Preface
Acknowledgments
About the Companion Website
1 Introduction
1.1 Reliability Concepts
1.1.1 Reliability in Our Lives
1.1.2 History of Reliability
1.1.3 Definition of Reliability
1.1.4 Quality and Reliability
1.1.5 The Importance of Reliability
1.2 Failure Concepts
1.2.1 Definition of Failure
1.2.2 Causes of Failure
1.2.3 Types of Failure Time
1.2.4 The Reliability Bathtub Curve
1.3 Summary
2 Basic Concepts of Probability
2.1 Probability
2.1.1 The Importance of Probability in Reliability
2.2 Joint Probability with Independence
2.3 Union Probability
2.4 Conditional Probability
2.5 Joint Probability with Dependence
2.6 Mutually Exclusive Events
2.7 Complement Rule
2.8 Total Probability
2.9 Bayes’ Rule
2.10 Summary
3 Lifetime Distributions
3.1 Probability Distributions
3.1.1 Random Variables
3.2 Discrete Probability Distribution
3.3 Continuous Probability Distribution
3.3.1 Reliability Concepts
3.3.2 Failure Rate
3.4 Exponential Distribution
3.4.1 Exponential Lack of Memory Property
3.4.2 Excel Practice
3.4.3 Minitab Practice
3.4.4 Python Practice
3.5 Weibull Distribution
3.5.1 Excel Practice
3.5.2 Minitab Practice
3.5.3 Python Practice
3.6 Normal Distribution
3.6.1 Excel Practice
3.6.2 Minitab Practice
3.6.3 Python Practice
3.7 Lognormal Distribution
3.7.1 Excel Practice
3.7.2 Minitab Practice
3.7.3 Python Practice
3.8 Summary
4 Reliability Data Plotting
4.1 Straight Line Properties
4.2 Least Squares Fit
4.2.1 Excel Practice
4.2.2 Minitab Practice
4.2.3 Python Practice
4.3 Linear Rectification
4.4 Exponential Distribution Plotting
4.4.1 Excel Practice
4.4.2 Minitab Practice
4.4.3 Python Practice
4.5 Weibull Distribution Plotting
4.5.1 Minitab Practice
4.5.2 Python Practice
4.6 Normal Distribution Plotting
4.6.1 Minitab Practice
4.6.2 Python Practice
4.7 Lognormal Distribution Plotting
4.7.1 Minitab Practice
4.7.2 Python Practice
4.8 Summary
5 Accelerated Life Testing
5.1 Accelerated Testing Theory
5.2 Exponential Distribution Acceleration
5.3 Weibull Distribution Acceleration
5.3.1 Minitab Practice
5.3.2 Python Practice
5.4 Arrhenius Model
5.4.1 Minitab Practice
5.4.2 Python Practice
5.5 Summary
6 System Failure Modeling
6.1 Reliability Block Diagram
6.2 Series System Model
6.3 Parallel System Model
6.4 Combined Serial–Parallel System Model
6.5 k-out-of-n System Model
6.6 Minimal Paths and Minimal Cuts
6.7 Summary
7 Repairable Systems
7.1 Corrective Maintenance
7.2 Preventive Maintenance
7.3 Mean Time between Failures
7.4 Mean Time to Repair
7.5 Availability
7.5.1 Inherent Availability
7.5.2 Achieved Availability
7.5.3 Operational Availability
7.5.4 System Availability
7.6 Maintainability
7.7 Preventive Maintenance Scheduling
7.7.1 Python Practice
7.8 Summary
8 Case Studies
8.1 Parametric Reliability Analysis
8.1.1 Description of Case Study
8.1.2 Minitab Practice
8.1.3 Python Practice
8.2 Nonparametric Reliability Analysis
8.2.1 Description of Case Study
8.2.2 Minitab Practice
8.2.3 Python Practice
8.3 Driverless Car Failure Data Analysis
8.3.1 Description of Case Study
8.3.2 Minitab Practice
8.3.3 Python Practice
8.4 Warranty Analysis
8.4.1 Description of Case Study
8.4.2 Minitab Practice
8.5 Stress–Strength Interference Analysis
8.5.1 Description of Case Study
8.5.2 Minitab Practice
8.5.3 Python Practice
8.6 Summary
Index
End User License Agreement
List of Tables
Chapter 01
Table 1.1 Description of quality and reliability.
Table 1.2 Description of hard and soft failures.
Table 1.3 Time to failure data of 10 components.
Chapter 03
Table 3.1 A probability mass function...
Table 3.2 The probability distribution for
X
.
Table 3.3 The failure characteristics with...
Chapter 04
Table 4.1 The coordinate data of
x
and
y
.
Table 4.2 Cumulative distribution function estimates...
Table 4.3 Median rank estimates of failure times.
Table 4.4 Median rank estimates of...
Table 4.5 The time to failure of 6 items.
Table 4.6 Readout failure data.
Table 4.7 Readout failure data and binomial estimates.
Table 4.8 Readout failure data and transformed CDF.
Table 4.9 Time to failure data of 17 components.
Table 4.10 Time to failure data of 10 components.
Table 4.11 Time to failure data of 15 components.
Chapter 05
Table 5.1 Various failure mechanisms and...
Table 5.2 Time to failure data...
Table 5.3 Readout data of failures...
Chapter 06
Table 6.1 The series system model...
Table 6.2 The parallel system model...
Chapter 08
Table 8.1 Time to failure data of loader tires.
Table 8.2 Failure data of furnace components.
Table 8.3 Disengagement data of Google vehicle.
Table 8.4 Disengagement data of Nissan vehicle.
Table 8.5 Disengagement data of Mercedes-Benz vehicle.
Table 8.6 Disengagement data of Volkswagen vehicle.
Table 8.7 Disengagement data of Bosch vehicle.
Table 8.8 Disengagement data of Delphi vehicle.
Table 8.9 Example of the warranty data format.
Table 8.10 Historical warranty data of vacuum products.
List of Illustrations
Chapter 01
Figure 1.1 The factor of 10 rule.
Figure 1.2 The adverse effects of failure.
Figure 1.3 Frequency distribution of...
Figure 1.4 Frequency distribution of...
Figure 1.5 Frequency distribution of...
Figure 1.6 Description of the...
Figure 1.7 Description of right...
Figure 1.8 Description of left-censored failure time.
Figure 1.9 Description of interval-censored failure time.
Figure 1.10 Data set with Minitab.
Figure 1.11 Python codes to...
Figure 1.12 Right-censored data...
Figure 1.13 The reliability bathtub...
Figure 1.14 Python codes used...
Figure 1.15 The reliability bathtub...
Chapter 02
Figure 2.1 Joint probability.
Figure 2.2 Union probability.
Figure 2.3 Mutually exclusive events.
Figure 2.4 Complement rule.
Figure 2.5 Total probability.
Chapter 03
Figure 3.1 Probability distribution of...
Figure 3.2 The probability density...
Figure 3.3 The probability density...
Figure 3.4 The cumulative distribution...
Figure 3.5 Exponential reliability function...
Figure 3.6 Probability Distribution Plot...
Figure 3.7 Vary Parameters function...
Figure 3.8 PDF of the...
Figure 3.9 PDF of the...
Figure 3.10 Python codes used...
Figure 3.11 The exponential distribution...
Figure 3.12 Python codes to...
Figure 3.13 Exponential CDF with...
Figure 3.14 Weibull probability density...
Figure 3.15 Weibull cumulative distribution...
Figure 3.16 Weibull cumulative distribution...
Figure 3.17 Weibull reliability function...
Figure 3.18 Weibull PDF with...
Figure 3.19 Weibull PDF with...
Figure 3.20 Python codes used...
Figure 3.21 The Weibull distribution...
Figure 3.22 The normal distribution.
Figure 3.23 The 68-95-99.7 rule of the normal distribution.
Figure 3.24 Normal cumulative distribution function.
Figure 3.25 Normal reliability function.
Figure 3.26 Normal failure rate function.
Figure 3.27 Standard normal distribution.
Figure 3.28 Normal distribution PDF setup.
Figure 3.29 Normal distribution PDF...
Figure 3.30 Python codes used...
Figure 3.31 The normal distribution...
Figure 3.32 Lognormal probability density...
Figure 3.33 Lognormal probability density...
Figure 3.34 Lognormal cumulative distribution...
Figure 3.35 Lognormal PDF setup...
Figure 3.36 Lognormal PDF with...
Figure 3.37 Python codes used...
Figure 3.38 The lognormal distribution...
Figure 3.39 Python codes for...
Figure 3.40 The distribution explorer...
Figure 3.41 The Python codes...
Figure 3.42 The top three...
Chapter 04
Figure 4.1 Properties of the...
Figure 4.2 Example of the...
Figure 4.3 Deviation of the...
Figure 4.4 A scatter plot...
Figure 4.5 A scatter plot...
Figure 4.6 Python codes for...
Figure 4.7 The least squares...
Figure 4.8 The CDF estimates...
Figure 4.9 A regression line...
Figure 4.10 Probability plot using...
Figure 4.11 Minitab worksheet...
Figure 4.12 Probability plot of the exponential...
Figure 4.13 Minitab data worksheet...
Figure 4.14 Probability plot of...
Figure 4.15 Python codes used...
Figure 4.16 The exponential probability...
Figure 4.17 The exponential probability...
Figure 4.18 The Weibull probability...
Figure 4.19 The data set...
Figure 4.20 The Weibull probability...
Figure 4.21 The Python codes...
Figure 4.22 The Weibull probability...
Figure 4.23 The Excel data...
Figure 4.24 The Python codes...
Figure 4.25 The Weibull probability...
Figure 4.26 The normal probability...
Figure 4.27 The normal probability...
Figure 4.28 The Python codes...
Figure 4.29 The normal probability...
Figure 4.30 The lognormal probability...
Figure 4.31 The data set...
Figure 4.32 The lognormal probability...
Figure 4.33 The Python codes...
Figure 4.34 The lognormal probability...
Chapter 05
Figure 5.1 Accelerated testing theory...
Figure 5.2 Minitab failure time...
Figure 5.3 Probability plots of...
Figure 5.4 Probability plots with...
Figure 5.5 Python codes were...
Figure 5.6 The Weibull distribution...
Figure 5.7 Minitab readout data...
Figure 5.8 Probability plots of...
Figure 5.9 Probability plots with...
Figure 5.10 Python codes to...
Figure 5.11 The AF in...
Chapter 06
Figure 6.1 The reliability block...
Figure 6.2 Series system model...
Figure 6.3 Parallel system model...
Figure 6.4 Combined serial–...
Figure 6.5 Combined serial–...
Figure 6.6 High-level and...
Figure 6.7 2-out-of...
Figure 6.8 A bridge structure.
Figure 6.9 RBD of a bridge structure.
Figure 6.10 Combined serial–...
Figure 6.11 Combined serial–...
Figure 6.12 RBD of the...
Figure 6.13 Combined serial–...
Figure 6.14 The fault tree diagram.
Figure 6.15 Converting the fault tree diagram to an RBD.
Figure 6.16 The bridge structure with five components.
Figure 6.17 The RBD of minimal cuts.
Figure 6.18 Combined serial–...
Chapter 07
Figure 7.1 Illustration of the...
Figure 7.2 The illustration of...
Figure 7.3 Reliability bathtub curve...
Figure 7.4 The cost related...
Figure 7.5 Python codes to...
Figure 7.6 The optical replacement...
Chapter 08
Figure 8.1 Data entry in...
Figure 8.2 Selection of distribution ID plot.
Figure 8.3 Distribution ID Plot-Right Censoring.
Figure 8.4 Distribution ID plot for time to failure (hour).
Figure 8.5 Selection of Distribution Overview Plot.
Figure 8.6 Distribution Overview Plot...
Figure 8.7 Distribution overview plot...
Figure 8.8 Selecting Parametric Distribution...
Figure 8.9 Parametric Distribution Analysis...
Figure 8.10 Parametric Distribution Analysis...
Figure 8.11 Parametric Distribution Analysis...
Figure 8.12 Parametric Distribution Analysis...
Figure 8.13 Cumulative Failure Plot...
Figure 8.14 Complete Python code...
Figure 8.15 Distribution ID plot.
Figure 8.16 Results for the...
Figure 8.17 Python codes of...
Figure 8.18 The distribution overview...
Figure 8.19 Python codes for...
Figure 8.20 The output of...
Figure 8.21 The exact and...
Figure 8.22 Nonparametric distribution analysis...
Figure 8.23 Nonparametric distribution analysis...
Figure 8.24 Nonparametric distribution analysis...
Figure 8.25 Nonparametric distribution analysis...
Figure 8.26 Survival plot with...
Figure 8.27 Minitab outputs of...
Figure 8.28 Python codes to...
Figure 8.29 Survival plot of...
Figure 8.30 Python output of...
Figure 8.31 Disengagement data set...
Figure 8.32 Distribution ID plot...
Figure 8.33 Distribution ID plot...
Figure 8.34 Distribution overview plot...
Figure 8.35 Distribution overview plot...
Figure 8.36 Merged data set...
Figure 8.37 Probability plots of...
Figure 8.38 Distribution overview plots...
Figure 8.39 Survival plot for...
Figure 8.40 Hazard plot for...
Figure 8.41 Python codes to...
Figure 8.42 Probability plots of...
Figure 8.43 Probability plots of...
Figure 8.44 Python codes to...
Figure 8.45 Distribution overview plots...
Figure 8.46 Warranty data set in Minitab.
Figure 8.47 Pre-process warranty data.
Figure 8.48 Pre-process warranty data setup.
Figure 8.49 Reformatted warranty data set.
Figure 8.50 Distribution ID plot.
Figure 8.51 Distribution ID plot setup.
Figure 8.52 Probability plots.
Figure 8.53 Warranty prediction.
Figure 8.54 Warranty prediction setup.
Figure 8.55 Warranty prediction results.
Figure 8.56 Summary of current warranty claims.
Figure 8.57 Table of predicted number of failures.
Figure 8.58 Predicted number of failures plot.
Figure 8.59 Probability Distribution Plot...
Figure 8.60 Two Distributions option...
Figure 8.61 Two distributions setup...
Figure 8.62 Stress and strength...
Figure 8.63 Python codes used...
Figure 8.64 The stress and...
Guide
Cover
Title page
Copyright
Table of Contents
About the Author
Preface
Acknowledgments
About the Companion Website
Begin Reading
Index
End User License Agreement
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About the Author
Jaejin Hwang is an associate professor of industrial and systems engineering at Northern Illinois University. He has been teaching reliability engineering and advanced quality control courses since 2016. He has actively used various software including Minitab, Excel, Python, SPSS, and Matlab to promote students’ learning. Dr Hwang holds a PhD in industrial engineering from Ohio State University. His research interests span the areas of quality and reliability, work measurement and work design, ergonomics, and occupational biomechanics. In 2022 he was nominated for the excellence in undergraduate teaching award at Northern Illinois University. He has authored over 50 technical papers published in peer-reviewed journals, international conference proceedings, and magazines. His book Data Analytics and Visualization in Quality Analysis Using Tableau was published by CRC Press in July 2021. Dr Hwang has been involved in numerous student graduation research and industrial projects. He is an executive committee member of the International Society for Occupational Ergonomics and Safety. He is an editorial board member of Work: A Journal of Prevention, Assessment & Rehabilitation and the Korean Society for Emotion and Sensibility. He is a guest editor for the special issue (November 2020–November 2022) in the International Journal of Environmental Research and Public Health.
Preface
Overview
Reliability is a vital and effective tool to analyze how long products and services can show satisfactory performance without failure. Today, we live in a society that uses complex and sophisticated physical and digital products. With the development of these technologies, the importance of the field of reliability is increasing.
This book is based on the statistical theory of how to analyze reliability. We want to quantitatively and accurately predict the reliability of products and services using various statistical distributions and probabilities. Reliability is the tool that helps to perform these analysis methods efficiently. Reliability tools allow us to process massive amounts of data quickly and automate our analysis methods efficiently. This book introduces you to how to perform reliability analysis using Minitab and Python.
