Detection and diagnosis of hybrid dynamic systems based on time fuzzy Petri nets

• Published in: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) Vol. 2; pp. 1825 - 1831 vol.2
• Main Authors: Loures, E.R., Pascal, J.-C.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Automata; Computer science; control theory; systems; Control theory. Systems; Equations; Exact sciences and technology; Fault detection; Fuzzy control; Fuzzy systems; Mathematical model; Modelling and identification; Monitoring; Petri nets; Production systems; Systems engineering and theory; Fuzzy logic; Fuzzy system; Change of state; Evolution equation; Qualitative analysis; Time interval; Dynamical system; Modeling; Fuzzy neural nets; Fault diagnostic; Petri net; Hybrid system
• ISBN: 0780385667; 9780780385665
• ISSN: 1062-922X; 2577-1655
• DOI: 10.1109/ICSMC.2004.1399920

This paper presents a detection and diagnosis method based on a qualitative model of the process. Starting from an identification process a fuzzy partitioning of the variables evolution is made, defining for each measured variable a number of qualitative states. Then time fuzzy intervals representing the moment of state change are defined. The process behaviour is represented by time fuzzy Petri nets (TFPN). The evolution of the model is the consequence of events detection due to the partitioning bounds crossing. According to the membership possibility of an event to the estimated time interval it is possible to reason about a fault occurrence. The fuzzy data issue from the TFPN components allows evaluating the causes of the fault - the diagnosis.