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Bayesian Networks In Fault Diagnosis: Practice And Application

By: (Edited by) China) Cai Baoping , (Edited by) China) Hu Jinqiu , (Edited by) China) Ji Renjie , (Edited by) China) Wu Shengnan , (Edited by) China) Liu Yonghong , (Edited by) China) Liu Zengkai

Extended Catalogue

Ksh 25,200.00

Format: Hardback or Cased Book

ISBN-10: 9813271485

ISBN-13: 9789813271487

Publisher: World Scientific Publishing Co Pte Ltd

Imprint: World Scientific Publishing Co Pte Ltd

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Oct 16th, 2018

Print length: 420 Pages

Product Classification: Bayesian inference

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  • Description

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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.

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.


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