A modified Bayesian network to handle cyclic loops in root cause diagnosis of process faults in the chemical process industry. (February 2022)

• Record Type: Journal Article
• Authors: Kumari, Pallavi; Bhadriraju, Bhavana; Wang, Qingsheng; Kwon, Joseph Sang-Il
• Is Part Of: Journal of process control. Volume 110(2022)
• Page Start: 84
• Page End: 98
• Publication Date: 2022-02
• Subjects: Cyclic loop -- Bayesian network -- Root cause diagnosis -- Transfer entropy; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.12.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In chemical processes, root cause diagnosis of process faults is highly crucial for efficient troubleshooting, since if poorly managed, process faults can lead to high-consequence rare events. For this purpose, Bayesian-based probabilistic models have been widely used because of their capability to capture causality in processes and perform root cause diagnosis. However, due to the acyclic nature of Bayesian networks, the existing probabilistic models do not account for presence of cyclic loops that are prevalent in chemical processes because of various control loops and coupling of process variables. Consequently, unaccountability of a high number of cyclic loops results in inaccurate root cause diagnosis. To improve the accuracy of root cause diagnosis, a modified Bayesian network (mBN) is proposed in this work that accounts for cyclic loops. Specifically, the mBN first identifies the weakest causal relation of a cyclic loop, and then converts it into a temporal relation. Because of this conversion, the mBN decomposes the cyclic network into an acyclic one over time horizon, thereby handling cyclic loops. Accounting for cyclic loops provides an improved structure of the causal network that aids in identifying correct causality. Finally, the performance of the proposed methodology is demonstrated through a case study of Tennessee Eastman process. Highlights: A modified Bayesian network is proposed for root cause diagnosis of process faults. The proposed network accounts for cyclic loops present in chemical processes. Forward–backward algorithm is used with alarm data for inference from the network. Performance of the proposed network is compared with the dynamic Bayesian network. Root causes are accurately diagnosed in the Tennessee Eastman process.