An Efficient Multiple Fault Detection and Isolation Scheme for Discrete Event System

The malfunction of sensors, actuators, and erroneous actions of human operators can have some disastrous consequences in high risk systems especially if these systems have multiple faults that can lead to undesirable shutdowns and consequently a plant output reduction. This paper introduced a new scheme to detect and isolate multiple faults in complex discrete event systems. This scheme comprises a set of discrete rules and an inference engine. The former characterizes the dynamic behavior of a process that is modeled by a Petri net and the latter infers the consequent part of the fired rules. The consequent parts are aggregated and compared with the original Petri net model of the process for detecting multiple faults. The proposed inference engine follows the forward reasoning strategy that fires transitions from the initial marking until the final marking is reached. Industrial processes are employed for testing the soundness of the proposed scheme.