Conflict Resolution Algorithms for Fault Detection and Diagnosis

Abstract

We present two approaches for conflict resolution between two fault detection schemes, detecting the same fault, via optimization with bounded adjustment of detection thresholds. In our first method, we assume initially that there is no conflict and optimize the thresholds of both schemes with respect to a partial cost function that penalizes false alarms and missed detections. Then we continuously update thresholds based on a comprehensive cost function that penalizes conflicts in addition to false alarms and missed detections. Our updates are bounded and controlled in such a way that the cost function always assumes the lowest possible cost as a function of thresholds. We make use of residual signals to minimize computational complexity. In our second method, we present a more general solution to the conflict resolution problem using a Markov Decision Process framework that generates an optimal policy for fault detection threshold. This method is computationally more complex but it is more general, does not require knowledge of residuals, and does not require initial optimization of the thresholds. We introduce an error signal that indicates failure in resolving the conflict using threshold updating in which case, a supervisor (human or computer) can be alerted and prompted to take a corrective action. We implemented our methods on a spacecraft attitude control thrustervalve system simulation with high noise. Our results show good performance and substantial reduction in conflicts under highly uncertain conditions.