Engineering Reliability and Risk Assessment: Advances in Reliability Science
Editat de Harish Garg, Mangey Ramen Limba Engleză Paperback – 30 sep 2022
- Uses case studies from industry practice to explain innovative solutions to real world risk assessment problems
- Addresses the full interdisciplinary range of topics that influence this complex field
- Provides brief introductions to important concepts, including risk and reliability analysis and fuzzy reliability
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Specificații
ISBN-13: 9780323919432
ISBN-10: 032391943X
Pagini: 282
Ilustrații: Approx. 100 illustrations
Dimensiuni: 191 x 235 mm
Greutate: 0.49 kg
Editura: ELSEVIER SCIENCE
Seria Advances in Reliability Science
ISBN-10: 032391943X
Pagini: 282
Ilustrații: Approx. 100 illustrations
Dimensiuni: 191 x 235 mm
Greutate: 0.49 kg
Editura: ELSEVIER SCIENCE
Seria Advances in Reliability Science
Cuprins
1. Bayesian networks for failure analysis of complex systems using different data sources 2. Hybrid failure modes and effect analysis model for reliability and safety evaluation of pressurized steam trap 3. Reliability and availability analysis of a standby system with activation time and varying demand 4. Fuzzy attack tree analysis of security threat assessment in an internet security system using algebraic t-norm and t-conorm 5. A new flexible extension to a lifetime distributions, properties, inference, and applications in engineering science 6. Markov and semi-Markov models in system reliability 7. Emerging trends and future directions in software reliability growth modelling 8. Reliability and profit analysis of a markov model having cost-free warranty with waiting repair facility 9. Semi-Markov modeling applications in system availability analysis 10. An a-cut interval-based similarity aggregation method to evaluate fault tree events for system safety under fuzzy environment 11. Business analytics to advance industrial safety management 12. Risk assessment and management of fire-induced domino effects in chemical industrial park 13. Stability assessment using Bayesian network control for inverters in smart grid