(588c) Discovery of Cyclic Loops in Bayesian Network for Root Cause Diagnosis of Process Faults
AIChE Annual Meeting
Thursday, November 17, 2022 - 8:38am to 8:57am
To this end, a direct transfer entropy (DTE)-based multiblock BN is proposed to discover cyclic loops in BN. First, the process is segmented into multiple blocks. Next, block-level BNs are learned using the DTE-based score and the Greedy search algorithm. DTE measures information transfer in the network by accounting for the effects of common source variables on parent and child variables. In doing so, DTE can distinguish if there exist direct or indirect causal relations between the parent and child variables, which was impossible to be done by the existing scores such as transfer entropy. Therefore, the DTE score provides a precise quantification of the fit of candidate networks to data, and thus aids in obtaining accurate block-level BNs. Although block-level BNs do not have cyclic loops, their fusion results in the discovery of cyclic loops. In this manner, the multiblock approach aids in the inclusion of significant cyclic loops into the BN for the process. Finally, the performance of the proposed methodology is demonstrated through a case study of the industrial benchmark Tennessee Eastman process.
Keywords: cyclic loop, Bayesian network, root cause diagnosis, direct transfer entropy
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