基于粗糙集的适应型Petri网故障诊断模型研究

针对Petri网应用存在模型状态空间复杂度问题,提出粗糙集与Petri网相结合的诊断算法,利用粗糙集的关联规则挖掘算法将包含繁冗信息的复杂系统简化,从而解决了Petri网的状态空间随着实际系统的规模增大而呈指数性增长的"知识爆炸"问题。在Petri网的数学理论基础上,提出了改进的关联矩阵和状态方程计算方法,提高了推理搜索速度,同时将诊断问题转化为矩阵运算,使复杂的推理转变为简单的数学计算。最后将该算法应用于直线感应电机参数优化诊断实例,证明了该算法的有效性和快速性。

Adaptive Petri nets modeling based on rough set for fault diagnosis

In order to resolve the problem that the Petri Net is restricted in the process of diagnosis in the complex system,Adaptive Petri Nets(APN) is ameliorated building fault diagnosis models aimed to accurately inspection troubles with some predigested incomplete and uncertain characteristic information of the monitoring system.Rough Set(RS) is adopted to deal with the flocks of data which is discrete,and the correlative information could be disinterred as using correlative rules mining.The composite algorithm exerting the preponderance in illation and diagnoses has expansive application potential.A parameter optimization and diagnosis example of the linear induction motor(SLIM) based on the theory is presented,the availability and efficiency of the method has been proved.