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E-grāmata: Reliability Analysis with Minitab

(Southern New Hampshire University, Manchester, USA), (Northeastern University, Boston, USA)
  • Formāts: 186 pages
  • Izdošanas datums: 23-Mar-2016
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781498737678
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Reliability Analysis with MinitabPalielināt
  • Formāts: 186 pages
  • Izdošanas datums: 23-Mar-2016
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781498737678
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Statistical Analysis for the Reliability Engineering Professional

Effectively conduct reliability analysis using the worlds 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 instruction on the use of Minitab. Minitab introduces reliability analysis tools that can be used to perform tasks that range from checking the distribution fit of lifetime data to estimating the warranty costs of a product.

Perform the Analyses Needed to Minimize Product Failures and Reduce Costs

Chock full of examples that include numerous case studies and over 200 screenshots, this book is a comprehensive guide to quality and reliability in the service and manufacturing industries. It illustrates the shapes of the most commonly used statistical distributions in reliability analysis, and in simple language demonstrates concepts that include parametric reliability analysis, nonparametric reliability analysis, warranty analysis, accelerated life testing, reliability test plans, and probit analysis.

Illustrating the application of Minitab for reliability analysis, this book explains how to:











Perform reliability analysis of a product with right-censored and exact failure time data Complete reliability analysis of a product with arbitrarily censored failure time data Achieve nonparametric reliability analysis of a product Predict the amount of money that is needed to cover the warranty costs for products in a specific period of time in the future Analyze the results from accelerated life testing on two different products Determine the reliability test sample size when the test time and the number of failures are constrained Regulate the testing time when test sample size and the number of failures are constrained Compare the reliabilities of parts from different vendors Test whether the reliability of a product depends on certain factors Predict the stress level at which a product will fail after a certain test period

Recenzijas

" a very good resource for both academicians and industry professionals with easy to understand applications and up-to-date definitions of relevant topics. I highly recommend it to both undergraduate and graduate students in engineering and management disciplines. The topics are applicable to service products as well as to manufacturing processes." Elif Kongar, University of Bridgeport, Connecticut, USA

"This book will be remembered as a handbook of reliability analysis using Minitab. Havent come across such a simple and effective means of teaching one on how to carry out such analysis using an industry wide popular software I would strongly encourage everyone working/studying reliability engineering to keep a copy on their shelves." Dr. Satish Nukala, Halliburton Energy Services

Preface xi
List of Figures
xiii
List of Tables
xxi
Acknowledgments xxiii
Authors xxv
Section I Introduction
1 Fundamental Concepts in Reliability Analysis
3(6)
1.1 Types of Failure Time
3(1)
1.2 Probability Functions in Reliability
4(2)
1.3 Book Outline
6(3)
2 Commonly Used Statistical Distributions in Reliability Analysis
9(16)
2.1 Smallest Extreme Value
9(2)
2.2 Weibull
11(1)
2.3 Three-Parameter Weibull
12(1)
2.4 Exponential
13(1)
2.5 Two-Parameter Exponential
14(1)
2.6 Normal
14(1)
2.7 Lognormal
15(1)
2.8 Three-Parameter Lognormal
16(1)
2.9 Logistic
17(1)
2.10 Loglogistic
18(1)
2.11 Three-Parameter Loglogistic
19(2)
2.12 Conclusion
21(4)
Section II Case Studies
3 Reliability Analysis with Right-Censored and Exact Failure Times
25(16)
3.1 Determination of Statistical Distribution
25(8)
3.2 Reliability Analysis
33(7)
3.3 Conclusion
40(1)
4 Reliability Analysis with Arbitrarily Censored Failure Times
41(24)
4.1 Determination of Statistical Distribution
42(4)
4.2 Reliability Analysis
46(7)
4.3 Additional Example
53(11)
4.4 Conclusion
64(1)
5 Nonparametric Reliability Analysis
65(12)
5.1 Nonparametric Analysis with Exact and Right-Censored Failure Times
66(6)
5.2 Nonparametric Analysis with Arbitrarily Censored Failure Times
72(4)
5.3 Conclusion
76(1)
6 Warranty Analysis
77(16)
6.1 Preprocessing of Raw Data
77(4)
6.2 Warranty Analysis
81(4)
6.3 Additional Example
85(6)
6.4 Conclusion
91(2)
7 Accelerated Life Testing
93(14)
7.1 Accelerated Life Testing and Analysis
93(8)
7.2 Additional Example
101(5)
7.3 Conclusion
106(1)
8 Reliability Test Plan with Constrained Test Time and Number of Failures
107(12)
8.1 Determination of Test Sample Size
107(4)
8.2 Additional Scenarios
111(6)
8.3 Conclusion
117(2)
9 Reliability Test Plan with Constrained Sample Size and Number of Failures
119(10)
9.1 Determination of Testing Time
119(4)
9.2 Additional Scenarios
123(5)
9.3 Conclusion
128(1)
10 Comparison of Reliability of Parts from Different Vendors
129(10)
10.1 Failure Time Data
129(1)
10.2 Regression Analysis
130(3)
10.3 Additional Example
133(4)
10.4 Conclusion
137(2)
11 Determination of Factors That Affect Product Reliability
139(12)
11.1 Failure Time Data
139(2)
11.2 Determination of Factors Affecting Reliability
141(3)
11.3 Additional Example
144(6)
11.4 Conclusion
150(1)
12 Prediction of Stress Levels That Cause Product Failure
151(6)
12.1 Probit Analysis
151(5)
12.2 Interpretation of Output
156(1)
12.3 Conclusion
156(1)
Bibliography 157(2)
Index 159
Kishore K. Pochampally is a professor at Southern New Hampshire University. He teaches lean six sigma, business analytics, project management, and operations management. He holds a Ph.D. in industrial engineering from Northeastern University. He is a Six Sigma Black Belt (ASQ), Project Management Professional (PMP®), and Certified Analytics Professional (CAP®).

Surendra M. Gupta is a professor of mechanical and industrial engineering and the director of the laboratory for responsible manufacturing at Northeastern University. He received his BE from Birla Institute of Technology and Science, MBA from Bryant University, and MSIE and Ph.D. from Purdue University. Dr. Guptas research interests span the areas of production/manufacturing systems and operations research. He has authored/coauthored well over 500 technical papers published in books, journals and international conference proceedings which have been cited worldwide by thousands of researchers. He is the recipient of outstanding research, industrial engineering professor, and doctoral dissertation advisor awards.
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