This paper investigates the problem of pattern failure safe diagnosis of decentralized fuzzy discrete event systems, and proposes a pattern failure safe diagnosis method based on pattern failure safe codiagnoser. Specifically, we first introduce the concepts of pattern failure and safe diagnosis of pattern failure in fuzzy discrete event systems, and then formalize the notion of safe codiagnosibility of pattern failure for fuzzy discrete event systems. Moreover, to verify the pattern failure safe codiagnosibility of decentralized fuzzy systems, we construct the pattern failure safe codiagnoser is constructed, and developa necessary and sufficient condition for the safe codiagnosibility of pattern failure. By doing these, the pattern failure safe diagnosis of decentralized fuzzy discrete event systems can be performed based on the pattern failure safe codiagnoser.

In this research, a fault prognosis method for stochastic discrete event systems (SDES) is presented using probabilistic labeled Petri nets (PLPN) . The goal is to enhance the accuracy of fault prognosis by using a Petri net model with probabilistic labels. Boundary and non-indicative markings are defined to develop this method. It can identify non-fault prefixes before faults occur, ensuring timely and accurate fault warnings within a limited number of steps. A verifier is constructed to transform the verification of fault predictability into a solvable model checking problem. This process yields a sufficient and necessary condition for the fault predictability of SDES. The proposed method offers a new perspective for fault prediction in SDES, enhancing system reliability and safety in practical applications.