Bayesian Networks in Fault Diagnosis
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
|Journal / Publication||IEEE Transactions on Industrial Informatics|
|Online published||19 Apr 2017|
|Publication status||Published - Oct 2017|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85031108435&origin=recordpage|
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.
- Bayesian networks (BNs), fault diagnosis