Bayesian Networks for Reliability Engineering - Brossura

Cai, Baoping; Liu, Yonghong; Liu, Zengkai; Chang, Yuanjiang; Jiang, Lei

Brossura

ISBN 10: 9811365180 ISBN 13: 9789811365188

Casa editrice: Springer, 2020

Riassunto

 

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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Informazioni sull?autore

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.

Dalla quarta di copertina

 

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.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 9811365180
• ISBN 13: 9789811365188
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 268
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Germania