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基于小波分析和Kohonen神经网络的滚动轴承故障分析
引用本文:张梅军,石文磊,赵亮,袁海龙.基于小波分析和Kohonen神经网络的滚动轴承故障分析[J].解放军理工大学学报,2011(2):161-164.
作者姓名:张梅军  石文磊  赵亮  袁海龙
作者单位:解放军理工大学工程兵工程学院;
摘    要:为了对旋转机械中滚动轴承的运行状态进行故障监测和诊断,在对振动信号进行采集和处理的基础上,提出了小波变换与Kohonen神经网络(SOM)相结合的滚动轴承故障诊断新方法.运用该方法在滚动轴承实验台上进行实验,用小波分析提取振动信号的特征值后,应用SOM网络对数据进行分类得到各种故障类型的标准样本,通过故障样本与标准样本...

关 键 词:故障诊断  小波分析  SOM网络  滚动轴承

Fault diagnosis based on wavelet analysis and self-organizing feature map of roller bearings
ZHANG Mei-jun,SHI Wen-lei,ZHAO Liang and YUAN Hai-long.Fault diagnosis based on wavelet analysis and self-organizing feature map of roller bearings[J].Journal of PLA University of Science and Technology(Natural Science Edition),2011(2):161-164.
Authors:ZHANG Mei-jun  SHI Wen-lei  ZHAO Liang and YUAN Hai-long
Institution:ZHANG Mei-jun,SHI Wen-lei,ZHAO Liang,YUAN Hai-long(Engineering Institute of Corps of Engineers,PLA Univ.of Sci.& Tech.,Nanjing 210007,China)
Abstract:To monitor and diagnose the faults of roller bearing in the rotating machinery,a new method was presented which combines wavelet analysis with SOM based on the collection and the disposal of the roller bearing vibration signals. Experiments were carried out on the roller bearings lab-table and the eigenvalue by wavelet analysis.The fault diagnosis result was obtained by contrasting and analyzing the fault and the standard stylebook.The result show that the method can identify and diagnose not only the runni...
Keywords:fault diagnosis  wavelet analysis  SOM(self-organizing feature map)  roller bearings  
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