Abstract—A new method for fault diagnosis of discrete event systems modeled by Neural Petri Nets (NPNs) is presented in this paper. Assuming that the PN structure and initial marking are known, faults are modeled by unobservable transitions. Neural networks (NNs) have important role to improve the method. The outputs of them are connected to unobservable transitions and yield the percentage of faults that may happen for prioritize the faults by online computation of the set of possible fault events. In this method the operator checks the fault that has more value at first. So we reduce the time that spends for repairing the system. Moreover, the graphical representation of the nets allows the diagnoser agent to compute off-line reduced portions of the net in order to improve the efficiency of the online computation, without a big increase in terms of memory requirement.
Index Terms—Discrete event systems (DES), fault diagnosis, Neural networks (NNs), Petri nets (PNs)
Roya Rangharanghi Hokmabad has been graduated from University of Tabriz (e-mail: email@example.com).
Mohammad Ali Badamchizadeh is with the Department of Control, Faculty of Electrical and Computer Engineering, University of Tabriz (e-mail: firstname.lastname@example.org).
Sohrab Khanmohammadi is with the Control Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz (e-mail:email@example.com).
Cite: R. Rangarangi Hokmabad, M. A. Badamchizadeh, and S. Khanmohammadi, "Fault Diagnosis of Discrete Event Systems Using Hybrid Petri Nets," International Journal of Computer Theory and Engineering vol. 4, no. 2, pp. 288-292, 2012.