FMS工作流故障诊断自适应加权模糊Petri网模型  

Adaptive weighted fuzzy Petri net for FMS workflow fault diagnosis

作　　者：潘海兰[1] 蒋文蓉[1] 吴嘉琪[1] 

机构地区：[1]上海第二工业大学计算机与信息学院,上海201209

出　　处：《计算机工程与设计》2011年第8期2841-2844,共4页Computer Engineering and Design

摘　　要：研究了使用人工神经网络和加权模糊Petri网对故障进行诊断的方法。针对传统Petri网难以精确地描述故障现象和故障原因之间的复杂关系,将人工神经网络、模糊逻辑和传统Petri网模型结合,定义了一种自适应的加权模糊Petri网模型以及模型的构造方法,在此基础上,提出了一种使用改进的BP算法对模型的权值进行训练的方法,并给出了采用构造的自适应模糊Petri网模型对故障进行诊断的具体步骤。最后对柔性制造系统(FMS)实例的故障进行诊断,验证了此自适应的加权模糊Petri网模型结合了Petri网和人工神经网络的优点,具有很强的故障推理能力以及自适应能力,能有效地对故障进行诊断。A method for using neural network and weighted fuzzy Petri net to diagnose fault is proposed. Aiming at the traditional Petri net can not predict precisely the complex relation ofthe default phenomenon and the cause, neuralnetwork, fuzzy logic and the traditional Petri net are combined, and a constructing method for adaptive weighted fuzzy Petri net model is proposed, and a improved BP algorism is introduced to train the weight of the model, then the specific process for using the model to diagnose the fault is given. Finally, the model is applied to the instance of FMS, and the model is proved to have the advantages of Petri net and neural network and have reasoning and adaptive ability.

关 键 词：故障诊断 柔性制造系统 神经网络 Petri网 权值 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]