Process system fault detection and diagnosis using a hybrid technique

This paper presents a hybrid methodology to detect and diagnose the faults in dynamic processes based on principal component analysis (PCA) with T2 statistics and a Bayesian network (BN). It deals with the uncertainty generated by the multivariate contribution plots and improves the diagnostic capacity by updating the BN with multiple likelihood evidence. It can diagnose the root cause of the process fault precisely as well as identify the fault propagation pathway. This methodology has been applied to the continuous stirred tank heater and the Tennessee Eastman chemical process for twelve fault scenarios. The result shows that it provides better diagnostic performance over conventional principal component analysis with hard evidence-based approaches.