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Bayesian networks for reliability en...
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Cai, Baoping.
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Bayesian networks for reliability engineering
Record Type:
Electronic resources : Monograph/item
Title/Author:
Bayesian networks for reliability engineering/ by Baoping Cai ... [et al.].
other author:
Cai, Baoping.
Published:
Singapore :Springer Singapore : : 2020.,
Description:
ix, 257 p. :ill., digital ;24 cm.
[NT 15003449]:
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.
Contained By:
Springer eBooks
Subject:
Bayesian statistical decision theory. -
Online resource:
https://doi.org/10.1007/978-981-13-6516-4
ISBN:
9789811365164
Bayesian networks for reliability engineering
Bayesian networks for reliability engineering
[electronic resource] /by Baoping Cai ... [et al.]. - Singapore :Springer Singapore :2020. - ix, 257 p. :ill., digital ;24 cm.
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.
ISBN: 9789811365164
Standard No.: 10.1007/978-981-13-6516-4doiSubjects--Topical Terms:
551404
Bayesian statistical decision theory.
LC Class. No.: QA279.5
Dewey Class. No.: 519.542
Bayesian networks for reliability engineering
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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.
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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.
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Intelligent Technologies and Robotics (Springer-42732)
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EB QA279.5
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