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Online fault diagnosis in partially observed Petri nets

Author

Listed:
  • Jiufu Liu
  • Zaihong Zhou
  • Zhisheng Wang

Abstract

This paper investigates the fault detection problem for discrete event systems (DES) which can be modelled by partially observed Petri nets (POPN). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, we propose an improved online fault diagnosis algorithm that integrates generalised mutual exclusion constraints (GMEC) and integer linear programming (ILP). We assume that the POPN structure and its initial markings are known, and the faults are modelled as unobservable transitions. First, the event sequence is observed and recorded. Then, the ILP problem of POPN is solved for elementary diagnosis of the system behaviour. While this system diagnoses that some faults may have happened, we also use GMEC for further diagnosis. Finally, we modelled and analysed an example of a real DES to test the new fault diagnoser. The proposed algorithm increased the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP was verified.

Suggested Citation

  • Jiufu Liu & Zaihong Zhou & Zhisheng Wang, 2018. "Online fault diagnosis in partially observed Petri nets," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 30(2), pages 205-218.
    • Handle: RePEc:ids:ijisen:v:30:y:2018:i:2:p:205-218
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