Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study

Abstract

To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, α, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals.

A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network

“…The reported DL methods for bearing fault diagnosis in noisy scenes can be divided into two groups: using signal processing techniques to remove noise from signal segments and adding additional components in the network. For the first group, using signal processing techniques to remove noise from signal segments such as shorttime Fourier transform [15], variational mode decomposition [16], singular value decomposition [17] and others [18][19][20][21]. For the second group, adding additional components in the network to enhance its ability to extract essential features in strong noisy scenes, such as soft thresholding [22], global context modules [23] and others [24][25][26][27][28].…”