Bayesian Control Loop Diagnosis by Combining Historical Data and Process Knowledge of Fault Signatures

Many performance monitoring algorithms (or monitors) have been developed to assess control performance and detect problems with specific components; however, these algorithms monitor single components as stand-alone experts and can be influenced by other problems that they were not meant to detect. Thus, the occurrence of a problem can lead to flood of abnormal monitor outputs and alarms which can be difficult to interpret. This work focuses on how to combine information from the many different monitoring algorithms and some of process knowledge in order to obtain a more reliable diagnosis. While traditional statistical or data-based methods need data from all abnormal cases that they should diagnose/isolate, this work focuses on how to improve the Bayesian control loop diagnosis by integrating process knowledge and training data when some of the abnormality data are sparse or not available in historical database. Simulation of the proposed Bayesian diagnostic system on the Tennessee Eastman challenge problem is presented. It is demonstrated that the diagnosis is possible even when there are no training data (or only few samples) from some abnormalities.