Optimal sensor selection for diagnosability enforcement of discrete event systems using labeled petri net

This paper addresses the problem of optimal sensor selection for ensuring diagnosability of discrete event systems. Given a nondiagnosable discrete event system modeled with labeled Petri nets, a new labeling function can be designed to enforce the system to be diagnosable. An ad-hoc parallel composition of non-deterministic finite automata (derived from the original labeled Petri net model) that are observed at multiple observation sites is employed. In order to optimize a given labeling function as well as a sensor selection, an integer linear programming problem is formulated to associate numerical sensor costs with transition relabelings. In a multi-fault system, this method can determine the type of faults that occur and the fault sequences pertaining to this fault type if multiple faults occur. Examples are presented to demonstrate the proposed method.

Automated summary

This paper addresses the issue of optimal sensor selection for ensuring diagnosability of discrete event systems by designing a new labeling function. An ad-hoc parallel composition of non-deterministic finite automata is used to observe the system at multiple observation sites. An integer linear programming problem is formulated to optimize the labeling function and sensor selection. This method can determine the fault type and corresponding fault sequences in a multi-fault system. Examples are provided to illustrate the proposed method.