基于小波减噪的滚动轴承故障频率的识别  

Fault Frequency Recognition of Rolling Bearing Based on Wavelet De-nosing

作　　者：田野[1] 侯跃谦[2] 李萌[2] 陆爽[2] 

机构地区：[1]长春工业大学机电工程学院,长春130012 [2]长春大学机械工程学院,长春130022

出　　处：《煤矿机械》2005年第6期141-142,共2页Coal Mine Machinery

基　　金：吉林省教育厅科研基金项目 (吉教合字 99第 10号 )

摘　　要：研究了振动信号局部奇异性在小波变换下的特性,定量分析了小波变换方法的减噪特性。根据滚动轴承故障振动信号和噪声的局部奇异性在小波变换下模极大值在不同尺度上的传播特性不同的特点,利用小波分解和重构算法,对轴承振动信号进行了分解、减噪、重构和谱分析。实验表明,小波减噪方法非常适于低信噪比情况下滚动轴承微弱振动信号的故障频率检测。The rolling bearing faint vibration signal local singularities under the wavelet transform are studied. From the point of view of faint signal detection, the de-noising character of the wavelet transform is qualitatively analyzed. According to the propagation features of modulus maximums of the rolling bearing fault signal and the noise under the wavelet transform different on the scales, and by using of the wavelet decomposition-reconstruction algorithm, the rolling bearing vibration signal is decomposed, de-noised, reconstructed and spectrum analyzed. The experiment shows that such method is capable of measurement of fault frequency of rolling bearing vibration signal.

关 键 词：小波变换 滚动轴承 故障频率 减噪 故障诊断 

分 类 号：TH137[机械工程—机械制造及自动化]