An Efficient Algorithm for K-Diagnosability Analysis of Bounded and Unbounded Petri Nets

In this paper, we propose a polynomial algorithm for conducting K-diagnosability analysis on both bounded and unbounded labeled Petri nets. More specifically, we formulate a sufficient condition for K-diagnosability by addressing the relaxation (in R) of an Integer Linear Programming (ILP) problem defined on a compacted horizon. In addition, if the model is K-diagnosable, the technique provides a value K-relax, potentially lower than K, that ensures (K-relax-)diagnosability. To assess the performance and efficiency of the developed method, a Petri net model of a railway benchmark is investigated.