Toward an efficient approach for diagnosability analysis of DES modeled by labeled Petri nets

This paper deals with the diagnosability of discrete event systems (DES) modeled by labeled Petri nets (LPNs). An additional parameter K ∈ ℕ, which is the number of observable events after an unobservable fault to ensure diagnosability, is discussed. With the incremental search of K, we transform the diagnosability problem into a series of K-diagnosability problems. For bounded diagnosable systems, K_min, the minimum value of K to ensure diagnosability, can be eventually found. The state space is generated on the fly, without investigation of unnecessary states. This is a notable advantage compared with some existing methods, since just a part of the state space can often be sufficient to assess diagnosability.