Fault Diagnosis of Discrete Event Systems Under Unknown Initial Conditions
Alejandro White, Ali Karimoddini, Rong Su · IEEE Transactions on Automatic Control · 2019
This paper proposes a novel diagnosis technique for discrete event systems (DESs) plant models. The developed diagnosis tool, so called diagnoser, is able to detect and isolate the occurrence of system's faults without the knowledge of the system's past behavior. This allows the diagnoser to asynchronously begin its diagnosis of a system's behavior at any time instance of system operation (including postfault occurrences); consequently removing the generally required synchronous initialization between a diagnoser and the system under diagnosis. The necessary and sufficient conditions are derived for the diagnosability of a given DES plant under this asynchronous situation. Several examples are provided to illustrate the details of the proposed diagnosis framework.