基于小波能量谱旋转机械振动信号的故障特征提取  

Fault Feature Extraction of Vibration Signal of Rotating Machinery Based on Wavelet Energy Spectrum

作　　者：游磊[1] 蒲强[1] 吴成宾[2] 

机构地区：[1]成都大学信息科学与技术学院,成都610106 [2]成都大学现代教育技术中心,成都610106

出　　处：《煤矿机械》2012年第5期271-273,共3页Coal Mine Machinery

基　　金：四川省教育厅科研项目(11ZA300);成都大学校科技基金项目(2011XJZ10)

摘　　要：旋转机械故障诊断的关键问题在于对振动故障信号的特征提取。利用小波能量谱分析方法能够发现不同分解层中的振动信号特征并分析出故障原因。基于小波能量谱方法能准确提取旋转机械的故障特征,尤其是对微弱故障信号,为正确判断故障提供了依据。实验验证了该方法在提取旋转机械振动故障信号方面的有效性和准确性。The key problem of fault diagnosis is to extract feature of fault signals.On the basis of wavelet energy spectrum for vibration signal,the signal features in different decomposition levels could be found after time domain analysis and frequency domain analysis according to energy distribution of fault signals.The experiments showed that extraction method based on wavelet energy spectrum has ability to extract accurately fault features of rotating machinery.The results proved that efficiency and precision of analysis method based on wavelet energy spectrum which is good at fault feature extraction for vibration signals of rotating machinery.

关 键 词：小波能量谱 振动信号 旋转机械 故障特征提取 

分 类 号：TH132[机械工程—机械制造及自动化]