To address the complex operating conditions and diverse fault types of electric mining truck drive motors, a fault diagnosis method integrating the Caterpillar Fungus Optimizer (CFO) with Support Vector Machine (SVM) is proposed. A parameter optimization model is constructed to jointly optimize the slack variable, penalty factor, and kernel function parameter of SVM, thereby improving fault recognition accuracy and model stability. Fault sample data are collected under typical operating conditions for identification experiments, and uniformly distributed random disturbances with different amplitudes are introduced to evaluate model robustness. Comparative analysis with several commonly used optimization methods demonstrates that the proposed approach exhibits strong adaptability in terms of fault classification accuracy, anti-interference capability, and model training stability. The average recognition accuracy reaches 98.67%, while the accuracy remains above 94.7% under disturbance conditions.

Electric mining truck  /  Drive motor  /  Fault diagnosis  /  CFO  /  SVM