Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 13, Issue 5, Pages 2227-2240Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2695583
Keywords
Bayesian networks (BNs); fault diagnosis
Categories
Funding
- Key Laboratory of Oil & Gas Equipment, Ministry of Education (Southwest Petroleum University) [OGE201403-24]
- National Natural Science Foundation of China [51309240]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130133120007]
- Fundamental Research Funds for the Central Universities [17CX05022, 14CX02197A]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT_14R58]
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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.
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