The Fault Diagnosis Model of FMS Workflow Based on Adaptive Weighted Fuzzy Petri Net
This paper proposes a method for using neural network and weighted fuzzy Petri net to diagnose fault. Aiming at the traditional Petri net can not precisely predict the complex relation of the default phenomenon and the cause, neural network, fuzzy logic and the traditional Petri net are combined, and a constructing method for adaptive weighted fuzzy Petri net model is proposed. Based on this, an improved BP algorism is introduced to train the weight of the model, and the specific process for using the model to diagnose the fault is given. Finally, the model was applied to the instance of FMS, and the model was proved to have the advantages of Petri net and neural network and have reasoning and adaptive ability.
Pengcheng Wang, Xiangdong Liu and Yongquan Han
H. L. Pan et al., "The Fault Diagnosis Model of FMS Workflow Based on Adaptive Weighted Fuzzy Petri Net", Advanced Materials Research, Vols. 605-607, pp. 837-843, 2013