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Full Description
Do you need to know what technique to use to evaluate the reliability of an engineered system? This self-contained guide provides comprehensive coverage of all the analytical and modeling techniques currently in use, from classical non-state and state space approaches, to newer and more advanced methods such as binary decision diagrams, dynamic fault trees, Bayesian belief networks, stochastic Petri nets, non-homogeneous Markov chains, semi-Markov processes, and phase type expansions. Readers will quickly understand the relative pros and cons of each technique, as well as how to combine different models together to address complex, real-world modeling scenarios. Numerous examples, case studies and problems provided throughout help readers put knowledge into practice, and a solutions manual and Powerpoint slides for instructors accompany the book online. This is the ideal self-study guide for students, researchers and practitioners in engineering and computer science.
Contents
Part I. Introduction: 1. Introduction; 2. Dependability evaluation; 3. Dependability metrics defined on a single unit; Part II. Non-State-Space Models (Combinatorial Models): 4. Reliability block diagram; 5. Network reliability; 6. Fault tree analysis; 7. State enumeration; 8. Dynamic redundancy; Part III. State-Space Models with Exponential Distributions: 9. Continuous time Markov chain: availability models; 10. Continuous time Markov chain: reliability models; 11. Continuous time Markov chain: queueing systems; 12. Petri nets; Part IV. State-Space Models with Non-Exponential Distributions: 13. Non-homogeneous CTMC; 14. Semi-Markov and Markov regenerative models; 15. Phase type expansion; Part V. Multi-Level Models; 16. Hierarchical models; 17. Fixed-point iteration; Part VI. Case Studies: 18. Case studies.
