Reliability and Risk Models: Setting Reliability Requirements (Hardcover)
5 Angebote vergleichen
Bester Preis: € 39,41 (vom 17.10.2016)1
Reliability and Risk Models: Setting Reliability Requirements (2005)
EN HCISBN: 9780470094884 bzw. 0470094885, in Englisch, Wiley, gebundenes Buch.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkostenfrei.
Von Händler/Antiquariat, Book Deals [60506629], Lewiston, NY, U.S.A.
This Book is in Good Condition. Clean Copy With Light Amount of Wear. 100% Guaranteed. Summary: 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. Applications.1.3.2 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 Rates.2.5 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 Sources.3.2 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 MODELS.4.1 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.5 LOADa?STRENGTH (DEMANDa?CAPACITY) MODELS.5.1 A General Reliability Model.5.2 The Loada?Strength Interference Model.5.3 Loada?Strength (Demand Capacity) Integrals.5.4 Calculating the Loada?Strength Integral Using Numerical Methods.5.5 Normally Distributed and Statistically Independent Load and Strength.5.6 Reliability and Risk Analysis Based on the Loada?Strength Interference Approach.5.6.1 Influence of Strength Variability on Reliability.5.6.2 The Importance of the Shape of the Lower Tail of the Strength Distribution and the Upper Tail of the Load Distribution.6 SOLVING RELIABILITY AND RISK MODELS USING A MONTE CARLO SIMULATION.6.1 Monte Carlo Simulation Algorithms.6.2 Simulation of Random Variables.6.2.1 Simulation of a Uniformly Distributed Random Variable.6.2.2 Generation of a Random Subset.6.2.3 Inverse Transformation Method for Simulation of Continuous Random Variables.6.2.4 Simulation of a Random Variable Following the Negative Exponential Distribution.6.2.5 Simulation of a Random Variable Following the Gamma Distribution.6.2.6 Simu.
Von Händler/Antiquariat, Book Deals [60506629], Lewiston, NY, U.S.A.
This Book is in Good Condition. Clean Copy With Light Amount of Wear. 100% Guaranteed. Summary: 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. Applications.1.3.2 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 Rates.2.5 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 Sources.3.2 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 MODELS.4.1 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.5 LOADa?STRENGTH (DEMANDa?CAPACITY) MODELS.5.1 A General Reliability Model.5.2 The Loada?Strength Interference Model.5.3 Loada?Strength (Demand Capacity) Integrals.5.4 Calculating the Loada?Strength Integral Using Numerical Methods.5.5 Normally Distributed and Statistically Independent Load and Strength.5.6 Reliability and Risk Analysis Based on the Loada?Strength Interference Approach.5.6.1 Influence of Strength Variability on Reliability.5.6.2 The Importance of the Shape of the Lower Tail of the Strength Distribution and the Upper Tail of the Load Distribution.6 SOLVING RELIABILITY AND RISK MODELS USING A MONTE CARLO SIMULATION.6.1 Monte Carlo Simulation Algorithms.6.2 Simulation of Random Variables.6.2.1 Simulation of a Uniformly Distributed Random Variable.6.2.2 Generation of a Random Subset.6.2.3 Inverse Transformation Method for Simulation of Continuous Random Variables.6.2.4 Simulation of a Random Variable Following the Negative Exponential Distribution.6.2.5 Simulation of a Random Variable Following the Gamma Distribution.6.2.6 Simu.
2
Reliability and Risk Models: Setting Reliability Requirements (2005)
EN HC NWISBN: 9780470094884 bzw. 0470094885, in Englisch, Wiley, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkostenfrei.
Von Händler/Antiquariat, Book Deals [60506629], Lewiston, NY, U.S.A.
Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 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. Applications.1.3.2 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 Rates.2.5 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 Sources.3.2 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 MODELS.4.1 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.5 LOADa?STRENGTH (DEMANDa?CAPACITY) MODELS.5.1 A General Reliability Model.5.2 The Loada?Strength Interference Model.5.3 Loada?Strength (Demand Capacity) Integrals.5.4 Calculating the Loada?Strength Integral Using Numerical Methods.5.5 Normally Distributed and Statistically Independent Load and Strength.5.6 Reliability and Risk Analysis Based on the Loada?Strength Interference Approach.5.6.1 Influence of Strength Variability on Reliability.5.6.2 The Importance of the Shape of the Lower Tail of the Strength Distribution and the Upper Tail of the Load Distribution.6 SOLVING RELIABILITY AND RISK MODELS USING A MONTE CARLO SIMULATION.6.1 Monte Carlo Simulation Algorithms.6.2 Simulation of Random Variables.6.2.1 Simulation of a Uniformly Distributed Random Variable.6.2.2 Generation of a Random Subset.6.2.3 Inverse Transformation Method for Simulation of Continuous Random Variables.6.2.4 Simulation of a Random Variable Following the Negative Exponential Distribution.6.2.5 Simulation of a Random Variable Following the Gamma Distribution.6.2.6 Simulation of a Random Va.
Von Händler/Antiquariat, Book Deals [60506629], Lewiston, NY, U.S.A.
Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 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. Applications.1.3.2 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 Rates.2.5 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 Sources.3.2 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 MODELS.4.1 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.5 LOADa?STRENGTH (DEMANDa?CAPACITY) MODELS.5.1 A General Reliability Model.5.2 The Loada?Strength Interference Model.5.3 Loada?Strength (Demand Capacity) Integrals.5.4 Calculating the Loada?Strength Integral Using Numerical Methods.5.5 Normally Distributed and Statistically Independent Load and Strength.5.6 Reliability and Risk Analysis Based on the Loada?Strength Interference Approach.5.6.1 Influence of Strength Variability on Reliability.5.6.2 The Importance of the Shape of the Lower Tail of the Strength Distribution and the Upper Tail of the Load Distribution.6 SOLVING RELIABILITY AND RISK MODELS USING A MONTE CARLO SIMULATION.6.1 Monte Carlo Simulation Algorithms.6.2 Simulation of Random Variables.6.2.1 Simulation of a Uniformly Distributed Random Variable.6.2.2 Generation of a Random Subset.6.2.3 Inverse Transformation Method for Simulation of Continuous Random Variables.6.2.4 Simulation of a Random Variable Following the Negative Exponential Distribution.6.2.5 Simulation of a Random Variable Following the Gamma Distribution.6.2.6 Simulation of a Random Va.
3
Reliability and Risk Models: Setting Reliability Requirements (2005)
EN HC USISBN: 9780470094884 bzw. 0470094885, in Englisch, John Wiley & Sons, gebundenes Buch, gebraucht.
Von Händler/Antiquariat, Anybook Ltd. [312675], Lincoln, United Kingdom.
This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket.
This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket.
4
Symbolbild
Reliability and Risk Models: Setting Reliability Requirements (Hardcover)
EN HC NWISBN: 9780470094884 bzw. 0470094885, in Englisch, Wiley, Vereinigte Staaten von Amerika, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkosten nach: USA.
Von Händler/Antiquariat, A Book Cart.
New. New- We ship from multiple locations. We ship Daily. Prompt customer service. 5-2.
Von Händler/Antiquariat, A Book Cart.
New. New- We ship from multiple locations. We ship Daily. Prompt customer service. 5-2.
Lade…