Integrating Probabilistic Reasoning and Statistical Quality Control Techniques for Fault Diagnosis in Hybrid Domains
Brian Ricks, Craig Harrison, Ole J. Mengshoel · Annual Conference of the PHM Society · 2011
Bayesian networks, which may be compiled to arithmetic circuits in the interest of speed and predictability, provide a probabilistic method for system fault diagnosis. Currently, there is a limitation in arithmetic circuits in that they can only represent discrete random variables, while important fault types such as drift and offset faults are continuous and induce continuous sensor data. In this paper, we investigate how to handle continuous behavior while using discrete random variables with a small number of states. Central in our approach is the novel integration of a method from statistical quality control, 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 Workshop on Principles of Diagnosis in 2009 and 2010 (DXC-09 and DXC-10). We demonstrate that PRODIAGNOSE, augmented with the CUSUM technique, is successful in diagnosing faults that are small in magnitude (offset faults) or drift linearly from a nominal state (drift faults). In one of these experiments, detection accuracy dramatically improved when CUSUM was used, jumping from 46.15% (CUSUM disabled) to 92.31% (CUSUM enabled).