Bayesian Networks in Fault Diagnosis

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 5; pp. 2227 - 2240
• Main Authors: Cai, Baoping, Huang, Lei, Xie, Min
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Bayesian analysis; Bayesian networks (BNs); Fault detection; Fault diagnosis; Inference algorithms; Learning systems; Maintenance; Mathematical model; Modelling; Probability
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2017.2695583

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.