Abstract：Ensuring the dependability, functionality, production effectiveness, and safety of mechanical systems necessitates assessing the condition and detecting faults in rolling bearings. However, this can be challenging due to the interference of background noise and other unstable factors. To address this issue, we propose using a weak selection approximate conjugate gradient pursuit method (WACGP) and an improved sine cosine algorithm (ISCA) for more effective extraction of bearing fault features. SCA includes an inertia weight and nonlinear parameter update approach to improve the efficiency and accuracy of sparse signal representation, while the ACGP has been modified to increase the speed and ability of identifying bearing fault characteristics. The validity of the method is confirmed by analyzing the bearing fault simulation signal and the actual vibration signal of the bearing's inner and outer ring. The proposed method outperforms the gradient pursuit algorithm based on sine cosine optimization in terms of efficiency and accuracy.
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