Bayesian Fault Detection and Diagnosis in Dynamic Systems

Knowledge Representation and Reasoning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 17

Uncertainty

We address two challenges in the task of tracking and diagnosing complex systems with mixtures of discrete and continuous variables: Accurate tracking and correct diagnosis of failures. This problem is a difficult one, particularly when the system dynamics are nondeterministic, not all aspects of the system are directly observed, and the sensors are subject to noise. In this paper, we propose a new approach to this task, based on the framework of hybrid dynamic Bayesian networks (DBN). We show that the DBN structure can be generated from a temporal causal graph. These models contain both continuous variables representing the state of the system and discrete variables representing discrete changes such as failures; these models can represent a variety of faults, including burst faults, measurement errors, and gradual drifts. We present a novel algorithm for tracking in hybrid DBNs, that deals with the challenges posed by this difficult problem. We demonstrate how the resulting algorithm can be used to detect faults in a complex hybrid system.