The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Bayesian Networks in Fault Diagnosis
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.
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Cites
(1988) · Cited by 16,582
Bayesian Networks and Decision Graphs
Jensen, F. V., & Nielsen, T. D. (2007) · Information Science and Statistics · doi:10.1007/978-0-387-68282-2 · Cited by 4,129
Big Data : The Management Revolution
マカフィー, ア., ブリニョルフソン, エ., & 有賀, 裕. (2013) · Harvard business review · Cited by 3,545
Gao, Z., Cecati, C., & Ding, S. X. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2417501 · Cited by 3,135
A review of process fault detection and diagnosis
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00160-6 · Cited by 2,723
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Lei, Y., Lin, J., He, Z., & Zuo, M. J. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.09.015 · Cited by 1,886
A review of process fault detection and diagnosis
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00162-x · Cited by 1,859
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2301773 · Cited by 1,716
Proceedings of the 8th Workshop on Programming Languages and Operating Systems
(2015) · doi:10.1145/2818302 · Cited by 1,628
A review of process fault detection and diagnosis
Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00161-8 · Cited by 1,530
Cited by
Improving the accuracy of medical diagnosis with causal machine learning
Richens, J. G., Lee, C. M., & Johri, S. (2020) · Nature Communications · doi:10.1038/s41467-020-17419-7 · Cited by 598
Fault Tree Analysis Improvements: A Bibliometric Analysis and Literature Review
Yazdi, M., Mohammadpour, J., Li, H., Huang, H., Zarei, E., Pirbalouti, R. G., & Adumene, S. (2023) · Quality and Reliability Engineering International · doi:10.1002/qre.3271 · Cited by 199
Artificial intelligence in safety-critical systems: a systematic review
Wang, Y., & Chung, S. H. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-07-2021-0419 · Cited by 96
Hybrid Intelligence Failure Analysis for Industry 4.0: A Literature Review and Future Prospective
Mokhtarzadeh, M., Rodríguez-Echeverría, J., Semanjski, I., & Gautama, S. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02376-5 · Cited by 35
Metadata
- Authors
- Cai, B., Huang, L., & Xie, M.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2017-10
- Volume
- 13
- Issue
- 5
- Pages
- 2227-2240
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2606788990
- Cited by
- 474
- References
- 88
- FWCI
- 29.52
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
