Detection of Bearing Fault in Induction Motors Based on Sparsity Auxiliary Adaptive Chirp Mode Decomposition

Stator current signals are widely adopted in bearing fault detection of induction motors. Nevertheless, in the case of incipient bearing faults, extracting weak fault features from the current signals remains a challenging task. This paper proposes an improved method using sparsity auxiliary adaptive chirp mode decomposition to address this problem. In this method, sparsity auxiliary is incorporated into the adaptive chirp mode decomposition framework, and constrained conditions are reconstructed. The alternating direction method of multipliers is employed to solve the convex optimization problem derived from the constrained conditions, thereby acquiring optimal decomposed modes and further realizing the extraction of weak features. Experimental results validate the efficacy and advantages of the developed method for bearing fault detection in induction motors.