04623nam 22006495 450 991048358000332120200703024029.0981-13-6516-410.1007/978-981-13-6516-4(CKB)4100000008525856(DE-He213)978-981-13-6516-4(MiAaPQ)EBC5721243(PPN)243768311(EXLCZ)99410000000852585620190228d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierBayesian Networks for Reliability Engineering /by Baoping Cai, Yonghong Liu, Zengkai Liu, Yuanjiang Chang, Lei Jiang1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (IX, 257 p. 153 illus., 125 illus. in color.) 981-13-6515-6 Bayesian networks for reliability -- Using Bayesian networks in reliability evaluation for subsea blowout preventer control system -- Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks -- Reliability evaluation of auxiliary feedwater system by mapping GO-FLOW models into Bayesian networks -- Dynamic Bayesian networks based performance evaluation of subsea blowout preventers in presence of imperfect repair -- Performance evaluation of subsea BOP control systems using dynamic Bayesian networks with imperfect repair and preventive maintenance -- Dynamic Bayesian network modelling of reliability of subsea blowout preventer stack in presence of common cause failures -- A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults -- Real-time reliability evaluation methodology based on dynamic Bayesian networks -- Reliability evaluation methodology of complex systems based on dynamic object-oriented Bayesian networks -- Bayesian network-based risk analysis methodology, a case of atmospheric and vacuum distillation unit -- A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels -- Availability-based engineering resilience metric and its corresponding evaluation methodology. This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.Computational intelligenceQuality controlReliabilityIndustrial safetyPower electronicsComputer simulationComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Quality Control, Reliability, Safety and Riskhttps://scigraph.springernature.com/ontologies/product-market-codes/T22032Power Electronics, Electrical Machines and Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24070Simulation and Modelinghttps://scigraph.springernature.com/ontologies/product-market-codes/I19000Computational intelligence.Quality control.Reliability.Industrial safety.Power electronics.Computer simulation.Computational Intelligence.Quality Control, Reliability, Safety and Risk.Power Electronics, Electrical Machines and Networks.Simulation and Modeling.006.3Cai Baopingauthttp://id.loc.gov/vocabulary/relators/aut1224833Liu Yonghongauthttp://id.loc.gov/vocabulary/relators/autLiu Zengkaiauthttp://id.loc.gov/vocabulary/relators/autChang Yuanjiangauthttp://id.loc.gov/vocabulary/relators/autJiang Leiauthttp://id.loc.gov/vocabulary/relators/autBOOK9910483580003321Bayesian Networks for Reliability Engineering2843963UNINA