BAYESIAN NETWORKS IN FAULT DIAGNOSIS

Cai Baoping

ISBN 10: 9813271485 / ISBN 13: 9789813271487

Published by World Scientific, 2018

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis.This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases.Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system.

Seller Inventory # 9789813271487

Bibliographic Details

• Title: BAYESIAN NETWORKS IN FAULT DIAGNOSIS
• Publisher: World Scientific
• Publication Date: 2018
• Language: English
• ISBN 10: 9813271485
• ISBN 13: 9789813271487
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 27D millimeters 235 millimeters width by 157 millimeters height by 27 millimeters depth
• Weight: 753 grams

About this title

Synopsis

Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis.This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases.Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system.