Bayesian filtering of the smearing effect: Fault isolation in chemical process monitoring

• An unsupervised fault isolation method is developed using Bayesian decision theory.
• Isolating faulty variables during fault propagation is addressed.
• The normalized RBCs are identical to the normalized traditional contributions.
• Faulty variables for multiple faults can be identified.

The isolation of faulty variables is a crucial step in the determination of the root causes of a process fault. Contribution plots, with their corresponding control limits, are the most popular tools used for isolating faulty variables. However, the isolation results may be misled by the smearing effect. In addition, the control limits of the contributions cannot be used to isolate faulty variables, as the control limits are obtained from normal operating data, which lack any information about the faults. In chemical processes, process faults rarely show random behavior; on the contrary, they will be propagated to different variables due to the actions of the process controllers. During the evolution of a fault, the task of isolating faulty variables needs to be concerned with the faulty variables identified at a previous time-point; in addition, the current decisions should influence the isolation results for the next sample when a fault constantly occurs. In the presented work, an unsupervised data-driven fault isolation method was developed based on Bayesian decision theory. Two fault scenarios of the Tennessee Eastman (TE) process were illustrated using visual comparative analysis to demonstrate how the different faulty variables were isolated when the fault evolved. In the industrial application, the proposed approach successfully located the faulty variables that were individually responsible for the simultaneous occurrence of multiple sensor faults and a process fault.