Compressive sensing (CS) theory allows measurement of sparse signals with a sampling rate far lower than the Nyquist sampling frequency. This could reduce the burden of local storage and remote transmitting. The periodic impacts generated in rolling element bearing local faults are obviously sparse in the time domain. According to this sparse feature, a rolling element bearing fault feature extraction method based on CS theory is proposed in the paper. Utilizing the shift invariant dictionary learning algorithm and the periodic presentation characteristic of local faults of roller bearings, a shift-invariant dictionary of which each atom contains only one impact pattern is constructed to represent the fault impact as sparsely as possible. The limited degree of sparsity is utilized to reconstruct the feature components based on compressive sampling matching pursuit (CoSaMP) method, realizing the diagnosis of the roller bearing impact fault. A simulation was used to analyze the effects of parameters such as sparsity, SNR and compressive rate on the proposed method and prove the effectiveness of the proposed method.

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