Reliability and Risk Models : Setting Reliability Requirements
Michael. Todinov
Bok Engelsk 2005 · Electronic books.
| Annen tittel | |
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| Utgitt | Hoboken : : Wiley, , 2005.
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| Omfang | 1 online resource (342 p.)
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| Opplysninger | Description based upon print version of record.. - RELIABILITY AND RISK MODELS; Contents; PREFACE; 1 SOME BASIC RELIABILITY CONCEPTS; 1.1 Reliability (survival) Function, Cumulative Distribution and Probability Density Function of the Times to Failure; 1.2 Random Events in Reliability and Risk Modelling; 1.2.1 Reliability of a System with Components Logically Arranged in Series; 1.2.2 Reliability of a System with Components Logically Arranged in Parallel; 1.2.3 Reliability of a System with Components Logically Arranged in Series and Parallel; 1.3 On Some Applications of the Total Probability Theorem and the Bayes Transform. - 1.3.1 Total Probability Theorem. Applications1.3.2 Bayesian Transform. Applications; 1.4 Physical and Logical Arrangement of Components; 2 COMMON RELIABILITY AND RISK MODELS AND THEIR APPLICATIONS; 2.1 General Framework for Reliability and Risk Analysis Based on Controlling Random Variables; 2.2 Binomial Model; 2.3 Homogeneous Poisson Process and Poisson Distribution; 2.4 Negative Exponential Distribution; 2.4.1 Memoryless Property of the Negative Exponential Distribution; 2.4.2 Link Between the Poisson Distribution and the Negative Exponential Distribution. - 2.4.3 Reliability of a Series Arrangement, Including Components with Constant Hazard Rates2.5 Hazard Rate; 2.5.1 Difference between a Failure Density and Hazard Rate; 2.6 Mean Time to Failure (MTTF); 2.7 Gamma Distribution; 2.8 Uncertainty Associated with the Mean Time to Failure; 2.9 Mean Time Between Failures (MTBF); 2.10 Uniform Distribution Model; 2.11 Gaussian (Normal) Model; 2.12 Log-Normal Model; 2.13 The Weibull Model; 2.14 Reliability Bathtub Curve for Non-Repairable Components/Systems; 2.15 Extreme Value Models; 3 RELIABILITY AND RISK MODELS BASED ON MIXTURE DISTRIBUTIONS. - 3.1 Distribution of a Property from Multiple Sources3.2 Variance of a Property from Multiple Sources; 3.3 Variance Upper Bound Theorem; 3.3.1 Determining the Source Whose Removal Results in the Largest Decrease of the Variance Upper Bound; 3.3.2 Modelling the Uncertainty of the Charpy Impact Energy at a Specified Test Temperature; 3.4 Variation and Uncertainty Associated with the Charpy Impact Energy at a Specified Test Temperature; Appendix 3.1; Appendix 3.2 An Algorithm for Determining the Upper Bound of the Variance of Properties from Sampling from Multiple Sources. - 4 BUILDING RELIABILITY AND RISK MODELS4.1 General Rules for Reliability Data Analysis; 4.2 Probability Plotting; 4.2.1 Testing for Consistency with the Uniform Distribution Model; 4.2.2 Testing for Consistency with the Exponential Model; 4.2.3 Testing for Consistency with the Weibull Distribution; 4.2.4 Testing for Consistency with the Type I Extreme Value Model; 4.2.5 Testing for Consistency with the Normal Distribution; 4.3 Estimating Model Parameters Using the Method of Maximum Likelihood; 4.4 Estimating the Parameters of a Three-Parameter Power Law. - 4.4.1 Some Applications of the Three-Parameter Power Law. - Presenting a radically new approach and technology for setting reliability requirements, this superb book also provides the first comprehensive overview of the M/F-FOP philosophy and its applications.* Each chapter covers probabilistic models, statistical and numerical procedures, applications and/or case studies* Comprehensively examines a new methodology for problem solving in the context of real reliability engineering problems* All models have been implemented in C++* The algorithms and programming code supplied can be used as a software toolbox for setting MFFOP* Case
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| Emner | |
| Sjanger | |
| Dewey | |
| ISBN | 0470094885
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