基于改进的LMD和总变差的滚动轴承故障诊断

对工业设备中的滚动轴承进行故障诊断时，被测信号经常受到高频噪声和间歇噪声的干扰，导致信号分解和特征提取的精度较低。为解决此问题，提出一种基于总变差降噪（TVD）和改进的局部均值分解（LMD）的方法。采取总变差方法对信号进行降噪处理，选取合适的正则化参数，使得降噪后的信号在具有高信噪比的同时具有较低的均方根误差。对降噪后的信号进行局部均值分解，根据互相关系值和峭度选取最佳的PF分量，进行包络分析，实现对故障特征的提取。对实测信号进行实验验证。结果表明：所提方法可以达到有效的降噪效果，能准确提取复杂振动信号中的故障特征。

Fault Diagnosis of Rolling Bearings Based on Improved LMD and Total Variation

For fault diagnosis of rolling bearings in industrial equipment,the measured signal is often disturbed by high frequency noise and intermittent noise,resulting in low accuracy of signal decomposition and feature extraction.To solve this problem,a method based on total variation denoising (TVD) and improved local mean decomposition (LMD),was proposed.The total variation method was adopted to reduce noise,and appropriate regularization parameter was selected,so that the noise reduced signal had a high signal-to-noise ratio (SNR) and a low root mean square error (RMSE).The local mean decomposition was performed on the noise-reduced signal,and the best PF components were selected according to the interrelationships value and the kurtosis value,and the envelope analysis was performed to realize the extraction of fault features.Experimental verification of the measured signals was performed.The results show that by using the method,an effective noise reduction effect can be achieved and the fault features in complex vibration signals can be accurately extracted.