A fault prognosis scheme for chemical reaction process using Pseudo-Bond Graph based Bayesian network

Bayesian network is an effective tool for fault prognosis. Learning the Bayesian network structure from data is, however, a difficult problem for complex industrial chemical processes. This paper presents an idea of jointly using Pseudo Bond Graph model and Bayesian network for fault prognosis. Pseudo Bond Graph is used to determine the Bayesian network structure, and the network parameters are learned from process data. An illustrative example via a CSTR system is presented. The results can show the feasibility and effectiveness of the proposed fault prognosis scheme.