摘要
SOM网络(自组织特征映射神经网络)模拟大脑神经系统,具有自适应、自学习与联想功能,是一种无导师学习网络,最大优点是能够保持原始数据的拓扑结构,在数据分类、知识获取、过程监控和故障识别等领域中应用广泛。将其用于电机转子的故障诊断,着重利用U矩阵图和D矩阵图等可视化工具对其分类结果进行仿真与分析,并与SOM网络一般聚类结果进行比较。结论表明,SOM网络的可视化方法简单、直观、易懂,对故障的判别率较高。
SOM network （ self - organizing feature map neural network ）learning with no instructors which has self - adaptive, self - learning features. The advantage is to maintain the topology of original data. It is in extensive application in the field of the data classification, knowledge acquisition, process monitoring wfault identification and so on. SOM network is used for rotor fault diagnosis. The U matrix map and D matrix is used as visualization tools to simulate and analyse the classification results , and it is compared with the general SOM network clustering results. The conclusion is that the SOM network visualization method is simple, intuitionistic and easy to understand, and has high rate in fault discrimination.
出处
《煤矿机械》
北大核心
2009年第3期190-192,共3页
Coal Mine Machinery
基金
国家自然科学基金资助项目(50575168)
关键词
自组织特征映射神经网络
可视化
电机转子
故障诊断
self- organizing feature map neural network
visualization
motor rotor
fault diagnosis
