基于最优小波基的轴承故障状态特征提取方法研究

摘要

In bearing fault diagnosis, select the optimal wavelet basis by calculating SUMVAR value. Noise reduction process on the bearing fault simulation signal and fault experimental signal with different wavelet basis , and then do some analysis indicators, such as the energy ratio of the noise reduction signal and the original signal, standard deviation of the noise reduction signal and the original signal, and kurtosis, and then the selected wavelet basis is the most optimal can be verified. The result shows that this method can extract fault feature frequency after doing Hilbert transform on the noise reduction signal with the optimal wavelet basis.%针对轴承故障诊断中最优小波基的选取问题,通过计算SUMVAR值选取最优小波基.用不同小波基对轴承故障仿真信号和故障实验信号进行降噪处理,分析降噪后信号与原信号的能量比值,降噪后信号与原信号标准差,峭度等指标,验证所选小波基的优越性.并对使用最优小波基降噪后信号做希尔伯特包络解调分析,结果表明,该方法能准确提取轴承故障特征频率.