Advances in Mathematical Modeling for ReliabilityT. Bedford Advances in Mathematical Modeling for Reliability discusses fundamental issues on mathematical modeling in reliability theory and its applications. Beginning with an extensive discussion of graphical modeling and Bayesian networks, the focus shifts towards repairable systems: a discussion about how sensitive availability calculations parameter choices, and emulators provide the potential to perform such calculations on complicated systems to a fair degree of accuracy and in a computationally efficient manner. Another issue that is addressed is how competing risks arise in reliability and maintenance analysis through the ways in which data is censored. Mixture failure rate modeling is also a point of discussion, as well as the signature of systems, where the properties of the system through the signature from the probability distributions on the lifetime of the components are distinguished. The last three topics of discussion are relations among aging and stochastic dependence, theoretical advances in modeling, inference and computation, and recent advances in recurrent event modeling and inference. |
Contents
Graphical Modeling and Bayesian Networks | 1 |
Repairable Systems Modeling | 32 |
Competing Risks | 63 |
Mixture Failure Rate Modeling | 96 |
Signature | 111 |
Relations Among Aging and Stochastic Dependence | 149 |
Theoretical Advances in Modeling Inference and Computation | 165 |
Recent Advances in Recurrent Event Modeling and Inference | 193 |
Point Estimation of the Transition Intensities for a Markov MultiState System via Output Performance Observation | 227 |
| 235 | |
| 237 | |
Other editions - View all
Advances in Mathematical Modeling for Reliability T. Bedford,J. Quigley,L. Walls Limited preview - 2008 |
Common terms and phrases
analysis Applied approach associated assume assumption authors availability Bayesian bivariate bounds calculate called cause competing risks components computation conditional consider consists continuous copula correlation corresponding cost defined denote density dependence derived described developed directed discounting discrete discussed distribution function Engineering estimate event example exchangeable expected exponential failed failure failure rate Figure given hazard rate Hence increasing independent inference intensity interest introduced IOS Press joint lifetimes lower main effects maintenance marginal Markov mean method mixture node Note notions observed obtained parameters performance possible posterior present probability problem Proof properties random variables rank References reliability repair represent respect sample Science sensitivity analysis signature Statistical stochastic structure testing Theorem theory tion transition unit University upper values vector virtual age