Fault Diagnosis Method of Micro-Motor Based on Jump Plus AM-FM Mode Decomposition and Symmetrized Dot Pattern

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions.