Fault Diagnosis for Discrete Event Systems Using Partially Observed Petri Nets

This study proposes a fault diagnosis method of discrete event systems on the basis of a Petri net model with partially observable transitions. Assume that the structure of the Petri net model and the initial marking are known, and the faults can be modeled by its unobservable transitions. One of the contributions of this work is the use of the structure information of Petri net to construct an online fault diagnoser which can describe the system behavior of normal or potential faults. By modeling the flow of tokens in particular places that contain fault information, the variation of tokens in these places may be calculated. The outputs and inputs of these places are determined to be enabled or not through analyzing some special structures. With the structure information, traversing all the states is not required. Furthermore, the computational complexity of the polynomial allows the model to meet real-time requirements. Another contribution of this work is to simplify the subnet model ahead of conducting the diagnostic process with the use of reduction rules. By removing some nodes that do not contain the necessary diagnostic information, the memory cost can be reduced.