基于无监督神经网络的故障模式识别  

Fault Pattern Recognition Based on Unsupervised Neural Network

作　　者：李伟[1] 钟飞[1] 何涛[1] 史铁林[1] 

机构地区：[1]湖北工业大学机械学院,湖北武汉430068

出　　处：《计算机测量与控制》2006年第6期742-744,764,共4页Computer Measurement &Control

基　　金：国家自然科学基金(50375047);湖北省教育厅重点项目(2003A002);湖北省自然科学基金(2004ABA064)

摘　　要：无监督学习的竞争式神经网络是一种数据聚类方法,能保持输入空间的拓扑关系不变,借助于一维或二维输出平面的一组有序的向量,实现高维数据的聚类和可视化;探讨了一种无监督神经网络———SOFM网络原理、思想和算法步骤,研究了无监督网络在模式识别中的应用,提出了基于SOFM网络的故障模式识别和状态监测方法;通过实例研究了SOFM网络在机械设备故障模式识别和状态监测中的应用。The unsupervised learning network is a data cluster method, it can transform an incoming signal pattern of arbitrary dimension into a one-or two-dimensional discrete map, and perform this transformation adaptively in a topologically ordered fashion. The prin- ciple and algorithm of a unsupervised neural network--SOFM is be discussed, the unsupervised network is applied to pattern recognition of the bearing fault, the new pattern recognition technique has been introduced, the high--dimensional input vectors are projected into a two-dimensional space. Numeric experimentation results show reasonable agreements to that the proposed method could recognize the fault pattern clearly and distinctly than common visualization method.

关 键 词：无监督神经网络 模式识别 故障诊断 

分 类 号：TM263[一般工业技术—材料科学与工程]