As systems like chemical plants, power plants, and automobiles get more complex, online diagnostic systems are becomingly increasingly important. One of the ways to rein in the complexity of describing and reasoning about large systems such as these is to describe them using qualitative rather than quantitative models.
Model-based diagnosis is a class of diagnostic techniques that use direct knowledge about how a system functions instead of expert rules detailing causes for every possible set of symptons of a broken system. Our research builds on standard methods for model-based diagnosis and extends them to the domain of complex dynamic systems described using qualitative models.
We motivate and describe out algorithm for diagnosing faults in a dynamic system given a qualitative model and a sequence of qualitative states. The main contributions in this algorithm include a method for propagating dependencies while solving a general constraint satisfaction problem, and a method for verfying the compatibility of a behavior with a model across time. The algorithm can diagnose multiple faults and uses models of faulty behavior, or behavioral modes.
We then demonstrate these techniques using an implemented program called QDOCS and test it on some realistic problems. Through our experiments with a model of the reaction control system (RCS) of the space shuttle and with a level-controller for a reaction tank, we show that QDOCS demonstrates the best balance of generality, accuracy and efficiency among known systems.
PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 128 pages. Also appears as Technical Report AI 95-239.