Model-based monitoring of dynamic systems

Abstract

Industrial process plants such as chemical refineries and electric power generation are examples of continuous-variable dynamic systems (CVDS) whose operation is continuously monitored for abnormal behavior. CVDSs pose a challenging diagnostic problem in which values are continuous (not discrete), relatively few parameters are observable, parameter values keep changing, and diagnosis must be performed while the system operates. We present a novel method for monitoring CVDSs which exploits the system's dynamic behavior for diagnostic clues. The key techniques are: modeling the physical system with dynamic qualitative/quantitative models, inducing diagnostic knowledge from qualitative simulations, continuously comparing observations against fault-model predictions, and incrementally creating and testing multiple-fault hypotheses. The important result is that the diagnosis is refined as the physical system's dynamic behavior is revealed over time. 1