Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

Abstract

Fault diagnosis of rolling bearings can effectively prevent sudden accidents and is an important factor for the safe operation of mechanical systems. However, traditional time–frequency analysis techniques cannot effectively obtain the fault feature information. In this paper, a flat variational modal decomposition denoising method based on wavelet transform and variational modal decomposition is proposed to solve susceptibility of vibration signal to noise interference and easily obtain fault features. In this method, first, a series of mother wavelets with different periods are designed based on tone-burst signals, in the decomposition process of variational modal decomposition. This method is based on the designed mother wavelet along with wavelet correlation coefficient for the elimination of the components that are superfluous and frequent from each intrinsic mode function. Then, the regression coefficients of the denoise components and the original signal are calculated, and we select the corresponding components with higher regression coefficients to reconstruct the signal. The reconstructed signal is taken as the new original signal to be decomposed again by variational modal decomposition, and the relevant components are analyzed by enveloping the spectrum, so as to effectively remove noise interference and ensure accurate acquisition of fault feature frequency. We apply this method to the rolling bearing fault data and a comparative study is made with variational modal decomposition and empirical mode decomposition algorithms. The results show that the signal-to-noise ratio of the signal is improved by 77% and 44% after being processed by the flat variational modal decomposition method, compared to the empirical mode decomposition and the variational modal decomposition methods.