Compound fault extraction method via self-adaptively determining the number of decomposition layers of the variational mode decomposition.

Abstract

Local mean decomposition (LMD) is a self-adaptive method, which has been widely applied to extract early fault signals from bearings. However, mode mixing occurs during the decomposition process. Moreover, in processing signals with strong noise, false frequency components can be generated by variational mode decomposition (VMD). To address these problems, a weak fault extraction method based on VMD is proposed for rolling bearings. This method regards LMD and the combination production function (CPF) as prefilters for VMD. First, LMD is used for denoising the original signal, and then the CPF components that contain the fault information are combined into a new signal. Second, this method determines the decomposition level K of the VMD from the spectral peaks of the recombined signal. Finally, this method decomposes the recombined signal using the VMD. The main contributions of the proposed method are (i) the CPF method is employed for adaptively de-noising, and the power of the fault feature can be improved; (ii) the decomposition level K of the VMD can be determined adaptively. After processing a simulated signal, fault information of the gears and rolling elements is successfully extracted, thereby demonstrating the feasibility of the presented method. Moreover, the feasibility of the proposed method is further demonstrated in a comparison of results with those obtained from the MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) algorithm.