Dynamic Bayesian Network Based Networked Process Monitoring for Fault Propagation Identification and Root Cause Diagnosis of Complex Dynamic Processes

Abstract In this article, a novel dynamic Bayesian network based networked process monitoring approach is proposed for fault detection, propagation pathway identification and root cause diagnosis. First, process network structure is designed according to the prior process knowledge including process flow sheets and used to characterize the causal relationships among different measurement variables. Then, the dynamic Bayesian network model parameters including the conditional probability density functions of different nodes are learned from historical process data to quantify the causality among those variables. Further, the new monitoring index is derived from the likelihoods of the entire process network for detecting abnormal operating events. With the captured process abnormality, the novel probabilistic contribution indices within Bayesian network are proposed to identify the major fault effect variables. Subsequently, the fault propagation pathways from the downstream backwards to upstream process are isolated through the variable contribution indices and hence the ending nodes of the identified pathways are determined as the root-cause variables of the abnormal events. The proposed approach is applied to the Tennessee Eastman Chemical process and the results show that the presented method can accurately detect abnormal events, identify fault propagation pathways, and diagnose the root-cause variables.