Title: Online fault diagnosis in partially observed Petri nets

Authors: Jiufu Liu; Zaihong Zhou; Zhisheng Wang

Addresses: College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China ' School of Information Engineering, Guangdong Medical University, Dongguan 523808,China ' College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China

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.

Keywords: fault diagnosis; partially observed Petri nets; POPN; integer linear programming; ILP; generalised mutual exclusion constraints; GMEC.

DOI: 10.1504/IJISE.2018.094843

International Journal of Industrial and Systems Engineering, 2018 Vol.30 No.2, pp.205 - 218

Received: 17 May 2016
Accepted: 15 Oct 2016
Published online: 25 Sep 2018 *

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