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E-raamat: Applied Reliability

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  • Formaat: 600 pages
  • Ilmumisaeg: 26-Aug-2011
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781439897249
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Applied ReliabilitySuurem pilt
  • Formaat: 600 pages
  • Ilmumisaeg: 26-Aug-2011
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781439897249
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Its been over 15 years since the publication of the 2nd edition of Applied Reliability. We continue to receive positive feedback from old users, and each year hundreds of engineers, quality specialists, and statisticians discover the book for the firsttime and become new fans. So why a 3rd edition? There are always new methods and techniques that update and improve upon older methods, but that was not the primary reason we felt the need to write a new edition. In the last 15 years, the ready availability of relatively 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 for even experts now can be solved with a few well-chosen clicks of a mouse. Additionally, with the quantitative solution often comes a plethora of graphics that aid in understanding and presenting the results. All this power comes with a price, however. Software documentation has had difficulty keeping up with the enhanced functionality added to new releases, especially in specialized areas such as reliability analysis. Also, in some cases different well-known software packages use different methods and output different answers. An analyst needs to know how to use these programs effectively and which methods are the most highly recommended. This information is hard to find for industrial reliability problems-- This popular book is an easy-to-use guide that addresses basic descriptive statistics, reliability concepts, the exponential distribution, the Weibull distribution, the lognormal distribution, reliability data plotting, acceleration models, life test data analysis and 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, it also provides detailed illustrations of software implementation throughout using several widely available software packages. Software and other files are available for download at www.crcpress.com-- Applied statisticians Tobias and Trindade have now left academia for commercial spheres, but update their textbook from the 1995 second edition to integrate the inexpensive and powerful statistics software now available. They use Minitab, which is widely used in schools, and JMP, which is used in high-technology companies. They also present solutions using popular spreadsheet programs, carefully pointing out what the software cannot do, and what it can. The topics include reliability concepts, exponential distribution, normal and lognormal distributions, physical acceleration models, alternative reliability models, repairable systems, and Bayesian reliability evaluation. Answers are provided to selected exercises. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com) 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

Arvustused

"I have used the second edition of this book for an Introduction to Reliability course for over 15 years. The third edition retains the features I liked about the second edition. In addition, it includes improved graphics [ and] examples of popular software used in industry There is a new chapter on Bayesian reliability and additional material on reliability data plotting, and repairable systems analysis. The book does a good and comprehensive job of explaining the basic reliability concepts; types of data encountered in practice and how to treat this data; reliability data plotting; and the common probability distributions used in reliability work, including motivations for their use and practical areas of application. an excellent choice for a first course in reliability. The book also does a good job of explaining more advanced topics the book will continue to be a popular desk reference in industry and a textbook for advanced undergraduate or first-year graduate students." Journal of the American Statistical Association, June 2014

Preface xiii
List of Figures xv
List of Tables xxvii
List of Examples xxxi
1 Basic Descriptive Statistics 1(28)
1.1 Populations and Samples
1(1)
1.2 Histograms and Frequency Functions
2(3)
1.3 Cumulative Frequency Function
5(1)
1.4 The Cumulative Distribution Function and the Probability Density Function
6(3)
1.5 Probability Concepts
9(7)
1.6 Random Variables
16(1)
1.7 Sample Estimates of Population Parameters
16(6)
1.8 How to Use Descriptive Statistics
22(1)
1.9 Data Simulation
23(2)
1.10 Summary
25(1)
Appendix 1A
26(1)
1.1A Creating a Step Chart in a Spreadsheet
26(1)
Problems
27(2)
2 Reliability Concepts 29(18)
2.1 Reliability Function
29(2)
2.2 Some Important Probabilities
31(1)
2.3 Hazard Function or Failure Rate
32(1)
2.4 Cumulative Hazard Function
33(1)
2.5 Average Failure Rate
34(1)
2.6 Units
35(1)
2.7 Bathtub Curve for Failure Rates
36(2)
2.8 Recurrence and Renewal Rates
38(1)
2.9 Mean Time to Failure and Residual Lifetime
39(2)
2.10 Types of Data
41(4)
2.10.1 Exact Times: Right-Censored Type I
41(1)
2.10.2 Exact Times: Right-Censored Type II
42(1)
2.10.3 Readout Time or Interval Data
42(1)
2.10.4 Multicensored Data
42(1)
2.10.5 Left-Censored Data
43(1)
2.10.6 Truncated Data
43(2)
2.11 Failure Mode Separation
45(1)
2.12 Summary
45(1)
Problems
46(1)
3 Exponential Distribution 47(40)
3.1 Exponential Distribution Basics
47(4)
3.2 The Mean Time to Fail for the Exponential
51(1)
3.3 The Exponential Lack of Memory Property
52(1)
3.4 Areas of Application for the Exponential
53(2)
3.5 Exponential Models with Duty Cycles and Failure on Demand
55(1)
3.6 Estimation of the Exponential Failure Rate X
56(2)
3.7 Exponential Distribution Closure Property
58(1)
3.8 Testing Goodness of Fit: The Chi-Square Test
59(3)
3.9 Testing Goodness of Fit: Empirical Distribution Function Tests
62(5)
3.9.1 D-Statistics: Kolmogorov-Smirnov
63(1)
3.9.2 W2-Statistics: Cramer-von Mises
64(1)
3.9.3 A2-Statistics: Anderson-Darling
64(3)
3.10 Confidence Bounds for X and the MTTF
67(2)
3.11 The Case of Zero Failures
69(2)
3.12 Planning Experiments Using the Exponential Distribution
71(4)
3.13 Simulating Exponential Random Variables
75(1)
3.14 The Two-Parameter Exponential Distribution
76(1)
3.15 Summary
77(1)
Appendix 3A
78(6)
3.1A Test Planning via Spreadsheet Functions
78(3)
Determining the Sample Size
78(2)
Determining the Test Length Using Spreadsheet Functions
80(1)
Determining the Number of Allowed Failures via Spreadsheet Functions
81(1)
3.2A EDF Goodness-of-Fit Tests Using Spreadsheets
81(6)
KS Test
81(3)
Problems
84(3)
4 Weibull Distribution 87(36)
4.1 Empirical Derivation of the Weibull Distribution
87(3)
4.1.1 Weibull Spreadsheet Calculations
90(1)
4.2 Properties of the Weibull Distribution
90(5)
4.3 Extreme Value Distribution Relationship
95(1)
4.4 Areas of Application
96(2)
4.5 Weibull Parameter Estimation: Maximum Likelihood Estimation Method
98(12)
4.6 Weibull Parameter Estimation: Linear Rectification
110(1)
4.7 Simulating Weibull Random Variables
111(1)
4.8 The Three-Parameter Weibull Distribution
112(1)
4.9 Goodness of Fit for the Weibull
113(1)
4.10 Summary
113(1)
Appendix 4A
114(7)
4.1A Using a Spreadsheet to Obtain Weibull MLEs
114(2)
4.2A Using a Spreadsheet to Obtain Weibull MLEs for Truncated Data
116(1)
4.3A Spreadsheet Likelihood Profile Confidence Intervals for Weibull Parameters
116(5)
Problems
121(2)
5 Normal and Lognormal Distributions 123(30)
5.1 Normal Distribution Basics
123(6)
5.2 Applications of the Normal Distribution
129(1)
5.3 Central Limit Theorem
130(1)
5.4 Normal Distribution Parameter Estimation
131(3)
5.5 Simulating Normal Random Variables
134(1)
5.6 Lognormal Life Distribution
135(1)
5.7 Properties of the Lognormal Distribution
136(4)
5.8 Lognormal Distribution Areas of Application
140(1)
5.9 Lognormal Parameter Estimation
141(5)
5.10 Some Useful Lognormal Equations
146(2)
5.11 Simulating Lognormal Random Variables
148(1)
5.12 Summary
148(1)
Appendix 5A
149(2)
5.1A Using a Spreadsheet to Obtain Lognormal MLEs
149(1)
5.2A Using a Spreadsheet to Obtain Lognormal MLEs for Interval Data
150(1)
Problems
151(2)
6 Reliability Data Plotting 153(40)
6.1 Properties of Straight Lines
153(2)
6.2 Least Squares Fit (Regression Analysis)
155(4)
6.3 Rectification
159(2)
6.4 Probability Plotting for the Exponential Distribution
161(14)
6.4.1 Rectifying the Exponential Distribution
162(1)
6.4.2 Median Rank Estimates for Exact Failure Times
163(1)
6.4.3 Median Rank Plotting Positions
164(4)
6.4.4 Confidence Limits Based on Rank Estimates
168(3)
6.4.5 Readout (Grouped) Data
171(1)
6.4.6 Alternative Estimate of the Failure Rate and Mean Life
172(1)
6.4.7 Confidence Limits for Binomial Estimate for Readout Data
172(3)
6.5 Probability Plotting for the Weibull Distribution
175(3)
6.5.1 Weibull Plotting: Exact Failure Times
176(2)
6.5.2 Weibull Survival Analysis via JMP
178(1)
6.5.3 Weibull Survival Analysis via Minitab
178(1)
6.6 Probability Plotting for the Normal and Lognormal Distributions
178(6)
6.6.1 Normal Distribution
178(3)
6.6.2 Lognormal Distribution
181(3)
6.7 Simultaneous Confidence Bands
184(3)
6.8 Summary
187(1)
Appendix 6A
187(4)
6.1A Order Statistics and Median Ranks
187(4)
Problems
191(2)
7 Analysis of Multicensored Data 193(48)
7.1 Multicensored Data
193(10)
7.1.1 Kaplan-Meier Product Limit Estimation
193(10)
7.2 Analysis of Interval (Readout) Data
203(6)
7.2.1 Interval (Readout) Data Analysis in JMP and Minitab
205(1)
7.2.2 Minitab Solution
206(1)
7.2.3 JMP Solution
206(3)
7.3 Life Table Data
209(4)
7.4 Left-Truncated and Right-Censored Data
213(4)
7.5 Left-Censored Data
217(3)
7.6 Other Sampling Schemes (Arbitrary Censoring: Double and Overlapping Interval Censoring)-Peto-Turnbull Estimator
220(3)
7.6.1 Current Status Data
220(3)
7.7 Simultaneous Confidence Bands for the Failure Distribution (or Survival) Function
223(8)
7.7.1 Hall-Wellner Confidence Bands
224(5)
7.7.2 Nair Equal Precision Confidence Bands
229(1)
7.7.3 Likelihood Ratio-Based Confidence Bands
229(1)
7.7.4 Bootstrap Methods for Confidence Bands
229(1)
7.7.5 Confidence Bands in Minitab and JMP
230(1)
7.8 Cumulative Hazard Estimation for Exact Failure Times
231(2)
7.9 Johnson Estimator
233(2)
Summary
235(1)
Appendix 7A
235(4)
7.1A Obtaining Bootstrap Confidence Bands Using a Spreadsheet
235(4)
Problems
239(2)
8 Physical Acceleration Models 241(60)
8.1 Accelerated Testing Theory
241(2)
8.2 Exponential Distribution Acceleration
243(1)
8.3 Acceleration Factors for the Weibull Distribution
244(12)
8.4 Likelihood Ratio Tests of Models
256(2)
8.5 Confidence Intervals Using the LR Method
258(2)
8.6 Lognormal Distribution Acceleration
260(5)
8.7 Acceleration Models
265(1)
8.8 Arrhenius Model
266(2)
8.9 Estimating AH with More than Two Temperatures
268(5)
8.10 Eyring Model
273(6)
8.11 Other Acceleration Models
279(2)
8.12 Acceleration and Burn-In
281(2)
8.13 Life Test Experimental Design
283(1)
8.14 Summary
284(1)
Appendix 8A
285(12)
8.1A An Alternative JMP Input for Weibull Analysis of High-Stress Failure Data
285(2)
8.2A Using a Spreadsheet for Weibull Analysis of High-Stress Failure Data
287(1)
8.3A Using A Spreadsheet for MLE Confidence Bounds for Weibull Shape Parameter
288(2)
8.4A Using a Spreadsheet for Lognormal Analysis of the High-Stress Failure Data Shown in Table 8.5
290(1)
8.5A Using a Spreadsheet for MLE Confidence Bounds for the Lognormal Shape Parameter
291(2)
8.6A Using a Spreadsheet for Arrhenius–Weibull Model
293(1)
8.7A Using a Spreadsheet for MLEs for Arrhenius–Power Relationship Lognormal Model
294(2)
8.8A Spreadsheet Templates for Weibull or Lognormal MLE Analysis
296(1)
Problems
297(4)
9 Alternative Reliability Models 301(44)
9.1 Step Stress Experiments
301(6)
9.2 Degradation Models
307(6)
9.2.1 Method 1
308(1)
9.2.2 Method 2
309(4)
9.3 Lifetime Regression Models
313(7)
9.4 The Proportional Hazards Model
320(1)
9.4.1 Proportional Hazards Model Assumption
320(1)
9.4.2 Properties and Applications of the Proportional Hazards Model
320(1)
9.5 Defect Subpopulation Models
321(14)
9.6 Summary
335(1)
Appendix 9A
335(7)
9.1A JMP Solution for Step Stress Data in Example 9.1
335(1)
9.2A Lifetime Regression Solution Using Excel
336(6)
9.3A JMP Likelihood Formula for the Defect Model
342(1)
9.4A JMP Likelihood Formulas for Example 9.7 Multistress Defect Model Example
342(1)
Problems
342(3)
10 System Failure Modeling: Bottom-Up Approach 345(24)
10.1 Series System Models
345(1)
10.2 The Competing Risk Model (Independent Case)
346(2)
10.3 Parallel or Redundant System Models
348(2)
10.4 Standby Models and the Gamma Distribution
350(2)
10.5 Complex Systems
352(4)
10.6 System Modeling: Minimal Paths and Minimal Cuts
356(4)
10.7 General Reliability Algorithms
360(2)
10.8 Burn-In Models
362(3)
10.9 The "Black Box" Approach: An Alternative to Bottom-Up Methods
365(2)
10.10 Summary
367(1)
Problems
367(2)
11 Quality Control in Reliability: Applications of Discrete Distributions 369(48)
11.1 Sampling Plan Distributions
369(8)
11.1.1 Permutations and Combinations
370(1)
11.1.2 Permutations and Combinations via Spreadsheet Functions
371(1)
11.1.3 The Binomial Distribution
372(2)
11.1.4 Cumulative Binomial Distribution
374(1)
11.1.5 Spreadsheet Function for the Binomial Distribution
375(1)
11.1.6 Relation of Binomial Distribution to Beta Distribution
376(1)
11.2 Nonparametric Estimates Used with the Binomial Distribution
377(1)
11.3 Confidence Limits for the Binomial Distribution
377(2)
11.4 Normal Approximation for Binomial Distribution
379(1)
11.5 Confidence Intervals Based on Binomial Hypothesis Tests
380(2)
11.6 Simulating Binomial Random Variables
382(2)
11.7 Geometric Distribution
384(1)
11.8 Negative Binomial Distribution
385(1)
11.9 Hypergeometric Distribution and Fisher's Exact Test
386(5)
11.9.1 Hypergeometric Distribution
386(1)
11.9.2 Fisher's Exact Test
387(2)
11.9.3 Fisher's Exact Test in IMP and Minitab
389(2)
11.10 Poisson Distribution
391(2)
11.11 Types of Sampling
393(7)
11.11.1 Risks
394(1)
11.11.2 Operating Characteristic Curve
395(1)
11.11.3 Binomial Calculations
395(1)
11.11.4 Examples of Operating Characteristic Curves
396(4)
11.12 Generating a Sampling Plan
400(6)
11.12.1 LTPD Sampling Plans
402(4)
11.13 Minimum Sample Size Plans
406(1)
11.14 Nearly Minimum Sampling Plans
406(1)
11.15 Relating an OC Curve to Lot Failure Rates
407(3)
11.16 Statistical Process Control Charting for Reliability
410(4)
11.17 Summary
414(1)
Problems
414(3)
12 Repairable Systems Part I: Nonparametric Analysis and Renewal Processes 417(54)
12.1 Repairable versus Nonrepairable Systems
417(2)
12.2 Graphical Analysis of a Renewal Process
419(5)
12.3 Analysis of a Sample of Repairable Systems
424(6)
12.3.1 Solution Using Spreadsheet Methods
428(2)
12.4 Confidence Limits for the Mean Cumulative Function (Exact Age Data)
430(5)
12.4.1 True Confidence Limits
430(5)
12.5 Nonparametric Comparison of Two MCF Curves
435(5)
12.6 Renewal Processes
440(1)
12.7 Homogeneous Poisson Process
441(5)
12.7.1 Distribution of Repair Times for HPP
442(4)
12.8 MTBF and MTTF for a Renewal Process
446(4)
12.9 MTTF and MTBF Two-Sample Comparisons
450(3)
12.10 Availability
453(2)
12.11 Renewal Rates
455(1)
12.12 Simulation of Renewal Processes
456(1)
12.13 Superposition of Renewal Processes
457(1)
12.14 CDF Estimation from Renewal Data (Unidentified Replacement)
458(4)
12.15 Summary
462(1)
Appendix 12A
462(7)
12.1A True Confidence Limits for the MCF
462(3)
12.2A Cox F-Test for Comparing Two Exponential Means
465(1)
12.3A Alternative Approach for Estimating CDF Using the Fundamental Renewal Equation
466(3)
Problems
469(2)
13 Repairable Systems Part II: Nonrenewal Processes 471(46)
13.1 Graphical Analysis of Nonrenewal Processes
471(3)
13.2 Two Models for a Nonrenewal Process
474(3)
13.3 Testing for Trends and Randomness
477(3)
13.3.1 Other Graphical Tools
478(2)
13.4 Laplace Test for Trend
480(2)
13.5 Reverse Arrangement Test
482(4)
13.6 Combining Data from Several Tests
486(2)
13.7 Nonhomogeneous Poisson Processes
488(1)
13.8 Models for the Intensity Function of an NHPP
489(10)
13.8.1 Power Relation Model
489(7)
13.8.2 Exponential Model
496(3)
13.9 Rate of Occurrence of Failures
499(1)
13.10 Reliability Growth Models
500(12)
13.11 Simulation of Stochastic Processes
512(3)
13.12 Summary
515(1)
Problems
515(2)
14 Bayesian Reliability Evaluation 517(24)
14.1 Classical versus Bayesian Analysis
517(5)
14.1.1 Bayes' Formula, Prior and Posterior Distribution Models, and Conjugate Priors
518(1)
14.1.2 Bayes' Approach for Analysis of Exponential Lifetimes
519(3)
14.2 Classical versus Bayes' System Reliability
522(1)
14.2.1 Classical Paradigm for HPP System Reliability Evaluation
522(1)
14.2.2 Bayesian Paradigm for HPP System Reliability Evaluation
522(1)
14.2.3 Advantages and Disadvantages of Using Bayes' Methodology
522(1)
14.3 Bayesian System MTBF Evaluations
523(6)
14.3.1 Calculating Prior Parameters Using the 50/95 Method
524(2)
14.3.2 Calculating the Test Time Needed to Confirm an MTBF Objective
526(3)
14.4 Bayesian Estimation of the Binomial p
529(3)
14.5 The Normal/Normal Conjugate Prior
532(1)
14.6 Informative and Noninformative Priors
533(3)
14.7 A Survey of More Advanced Bayesian Methods
536(1)
14.8 Summary
537(1)
Appendix 14A
538(1)
14.1A Gamma and Chi-Square Distribution Relationships
538(1)
Problems
538(3)
Answers to Selected Exercises 541(10)
References 551(6)
Index 557
Dr. David C. Trindade is the chief officer of best practices and fellow at Bloom Energy. He was previously a distinguished principal engineer at Sun Microsystems, senior director of software quality at Phoenix Technologies, senior fellow and director of reliability and applied statistics at Advanced Micro Devices, worldwide director of quality and reliability at General Instruments, and advisory engineer at IBM. He has also been an adjunct lecturer at the University of Vermont and Santa Clara University, teaching courses in statistical analysis, reliability, probability, and applied statistics. In 2008, he was the recipient of the IEEE Reliability Societys Lifetime Achievement Award.
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