Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

This paper develops a methodology to combine diagnostic information from various fault detection and isolation tools to diagnose the true root cause of an abnormal event in industrial processes. Limited diagnostic information from kernel principal component analysis, other online fault detection and diagnostic tools, and process knowledge were combined through Bayesian belief network. The proposed methodology will enable an operator to diagnose the root cause of the abnormality. Further, some challenges on application of Bayesian network on process fault diagnosis such as network connection determination, estimation of conditional probabilities, and cyclic loop handling were addressed. The proposed methodology was applied to Fluid Catalytic Cracking unit and Tennessee Eastman Chemical Process. In both cases, the proposed approach showed a good capability of diagnosing the root cause of abnormal conditions.