A ROBUST EVENT-ORIENTED METHODOLOGY FOR DIAGNOSIS OF DYNAMIC PROCESS SYSTEMS

The Model-Integrated Diagnostic Analysis System (MIDAS) is a program for diagnosing abnormal transient conditions in chemical, refinery and utility systems. MIDAS determines the root causes of disturbances using an event model that expresses process causality and conditions for violation of quantitative process constraint equations. Root causes typically considered are equipment degradation and failure, sensor failure, incorrect operation and external disturbances. The diagnostic algorithm utilizes the event model to construct clusters of related events, each cluster attributable to a single malfunction. The algorithm is designed to handle practical problems such as unreliable sensors, variations in the order of symptom detection, complex dynamics induced by feedback and process controllers, and multiple simultaneous malfunctions. Temporal features in the dynamic propagation of a malfunction are employed, and the diagnosis is incrementally evolved as disturbances appear and abate. The algorithm has been applied to a case study of a continuous reaction process. Results show MIDAS to be accurate in 99% of the cases studied, dealing successfully with many instances of complex process dynamics and out-of-order events.