Diagnosis and diagnosability analysis of labeled Petri nets using reduction rules

Abstract

This paper addresses the combinatorial explosion problem for diagnosability analysis of discrete event systems (DESs). Some reduction rules are given to simplify a priori the labeled Petri net (LPN) model before analyzing the diagnosability. When the conditions of these reduction rules are satisfied, some regular unobservable transitions and some places, that do not contain necessary information for the diagnosability analysis, are removed. It is proved the diagnosability of the initial LPN is preserved by using these reduction rules. In this paper, the “diagnoser” approach is used to compare the diagnosability analysis of the initial LPN model and that of the reduced LPN model. By using reduction rules, the memory cost for diagnosability analysis is reduced.