Fault Diagnosis Method Based on Joint Noise Reduction

In response to the problem that the fault characteristics of rolling bearings are easily masked by noise, this paper proposes a new noise reduction method. Firstly, the bearing signal features are used to select kurtosis and correlation coefficient to improve variational mode decomposition (IVMD) and decompose the signal into K modal components; Secondly, establish relevant classification indicators to divide modal components into two categories; Finally, adaptive wavelet threshold denoising (A WTD) and Savitzky Golay filtering (SG) were used to process the two types of modal components. Through actual signal analysis, it has been proven that this method can improve the signal-to-noise ratio and has stronger feature extraction ability for faulty bearing signals compared to other denoising methods.