Integrating probabilistic reasoning and statistical quality control techniques for fault diagnosis in hybrid domains

Bayesian networks, which may be compiled to arithmetic cir-cuits in the interest of speed and predictability, provide a prob-abilistic method for system fault diagnosis. Currently, there is a limitation in arithmetic circuits in that they can only rep-resent discrete random variables, while important fault types such as drift and offset faults are continuous and induce con-tinuous sensor data. In this paper, we investigate how to handle continuous behavior while using discrete random vari-ables with a small number of states. Central in our approach is the novel integration of a method from statistical quality con-trol, known as cumulative sum (CUSUM), with probabilistic reasoning using static arithmetic circuits compiled from static Bayesian networks. Experimentally, an arithmetic circuit model of the ADAPT Electrical Power System (EPS), a real-world EPS located at the NASA Ames Research Center, is considered. We report on the validation of this approach using PRODIAGNOSE, which had the best performance in three of four industrial track competitions at the International Work