基于小世界模糊联想记忆模型的电机故障诊断研究  

Research of Motor Fault Diagnosis Based on Small-world Fuzzy Associative Memory Model

作　　者：张强[1] 苏扬[1] 孟超[1] 王莉[1] 

机构地区：[1]空军工程大学,西安710051

出　　处：《大电机技术》2015年第3期16-19,共4页Large Electric Machine and Hydraulic Turbine

摘　　要：将小世界网络体系与神经网络融合设计,提出了一种基于小世界体系的模糊联想记忆模型,采用伪逆学习规则设计联想记忆权值,然后计算权值模糊隶属度,最后动态构造小世界神经网络模型。针对电机故障诊断进行了应用研究,仿真结果验证了本文所提算法的模式存储和联想记忆的性能最优性。This paper presents a small-world fuzzy associative memory model which is associated small-world complex networks with neural networks. Firstly, the model is designed weight of associative memory with pseudo inverse method. Secondly, the fuzzy membership of weight is calculated. Finally, a small-world complex networks model is constructed with dynamic method.Fault diagnosis of motor is studied and simulated with the new model and algorithm, the result shows the optimal performanceof of pattern memory and associative memory.

关 键 词：小世界网络 模糊联想记忆 电机 故障诊断 

分 类 号：TM307[电气工程—电机]