Bayesian Networks in Fault Diagnosis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

247 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number7904628
Pages (from-to)2227-2240
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume13
Issue number5
Online published19 Apr 2017
Publication statusPublished - Oct 2017

Abstract

Fault diagnosis is useful in helping technicians detect, isolate, and 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 paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Research Area(s)

  • Bayesian networks (BNs), fault diagnosis

Citation Format(s)

Bayesian Networks in Fault Diagnosis. / Cai, Baoping; Huang, Lei; Xie, Min.

In: IEEE Transactions on Industrial Informatics, Vol. 13, No. 5, 7904628, 10.2017, p. 2227-2240.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review