Fault Diagnosis and Prognosis With Partially Observed Petri Nets

This paper concerns the prevention of faults in discrete event systems modeled with partially observed Petri nets (POPNs) that include the definition of sensors used to measure the events and markings. Observation sequences result from this modeling, and the firing sequences and initial marking consistent with a given observation sequence are systematically obtained. The degree of confidence of past and future states and events are computed: state estimation fault diagnosis and fault prediction result from this computation. Finally, diagnosability, detectability, and predictability are defined for observation sequences and POPNs and are discussed with respect to the sensor configuration.