Bayesian Networks for Reliability Engineering
Published on 8. March 2019
Book
Hardback
IX, 257 pages
978-981-13-6515-7 (ISBN)
Description
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.
More details
Edition
2020 ed.
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
125 farbige Abbildungen, 28 s/w Abbildungen
IX, 257 p. 153 illus., 125 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
571 gr
ISBN-13
978-981-13-6515-7 (9789811365157)
DOI
10.1007/978-981-13-6516-4
Schweitzer Classification
Other editions
Additional editions
Baoping Cai | Yonghong Liu | Zengkai Liu
Bayesian Networks for Reliability Engineering
Book
08/2020
Springer
€160.49
Shipment within 15-20 days
Baoping Cai | Yonghong Liu | Zengkai Liu
Bayesian Networks for Reliability Engineering
E-Book
02/2019
1st Edition
Springer
€149.79
Available for download
Persons
Baoping Cai is an associate professor at the China University of Petroleum (East China), a visiting researcher of the "Hong Kong Scholar" program at the City University of Hong Kong, and a visiting researcher at the Norwegian University of Science and Technology. He is an associate editor of IEEE Access (SCI journal) and Human-Centric Computing and Information Sciences (SCI journal), an editorial board member of 3 international journals, and a leading guest editor of 1 international journal. His research interests include reliability engineering, fault diagnosis, risk analysis, and Bayesian networks methodology and application. Up to now, he has published 65 SCI-index journal papers, 4 monographs, and holds 37 patents.
Content
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.