A Bearing Fault Feature Extraction Strategy Based on Finite Frequency Shift-Invariant Sparse Coding

Suffering from strong background noise, sparse representation cannot effectively reveal signal implicit features, which brings challenges in identifying mechanical fault. Therefore, a finite frequency shift-invariant sparse feature extraction strategy (FF-SISFES) for signal feature enhancement is proposed in this article. The strategy consists of two parts. First, a differential envelope energy (DEE) extraction method based on collaborative filtering (DEE-CF) is proposed. It is employed for estimating sensitive frequency band and extracting feature wavelet corresponding to the subband signal with the highest energy. Second, to effectively obtain objected feature and greatly reduce the interference of background noise, a feature wavelet enhancement generalized minimax-concave convolution dictionary learning (FWE-GMCCDL) method is developed. Wherein, the DEE is utilized for obtaining sparse parameter. Finally, feature frequencies contained in sparse coefficients are extracted by envelope spectrum. The experimental results verify that the FF-SISFES can effectively learn and enhance shift-invariant feature from observed signal. The proposed method has better ability of fault extraction than available dictionary learning methods, such as Laplace correlation filtering, group-sparsity learning (GSL), periodic GSL (P-GSL), and traditional convolutional dictionary learning (CDL).