Structured sparsity assisted online convolution sparse coding and its application on weak signature detection

Abstract

Due to the strong background noise and the acquisition system noise, the useful characteristics are often difficult to be detected. To solve this problem, sparse coding captures a concise representation of the high-level features in the signal using the underlying structure of the signal. Recently, an Online Convolutional Sparse Coding (OCSC) denoising algorithm has been proposed. However, it does not consider the structural characteristics of the signal, the sparsity of each iteration is not enough. Therefore, a threshold shrinkage algorithm considering neighborhood sparsity is proposed, and a training strategy from loose to tight is developed to further improve the denoising performance of the algorithm, called Variable Threshold Neighborhood Online Convolution Sparse Coding (VTNOCSC). By embedding the structural sparse threshold shrinkage operator into the process of solving the sparse coefficient and gradually approaching the optimal noise separation point in the training, the signal denoising performance of the algorithm is greatly improved. VTNOCSC is used to process the actual bearing fault signal, the noise interference is successfully reduced and the interest features are more evident. Compared with other existing methods, VTNOCSC has better denoising performance.