Fault Diagnosis in Discrete Event Systems Modeled by Signal Interpreted Petri Nets

Abstract

This paper proposes a new approach for detecting and isolating faults in manufacturing systems using fault-free models of the plant and the controller. The plant and the controller are both modeled by signal interpreted Petri nets. It is shown how both models can be used to predict sensor and actuator signals. By comparing the predicted sensor signals to the observed sensor signals, the occurrence of a fault can be inferred. Further information about the fault can be obtained by determining the actuator signals required by the plant to generate the observed sensor signals and comparing these signals to the expected actuator signals. The proposed approach has a linear time complexity.