Intelligent Gear Fault Diagnosis for Dual-signal Fusion Based on the Stator Current and the Electromagnetic Torque

In the motor driven gear transmission system, the motor body has the characteristics of sensors, so the stator current and electromagnetic torque signal of the motor can be used to analyze the gear fault. Due to the influence of the speed and load torque, the accuracy of the fault diagnosis results is low. Aiming at this problem, a gear fault diagnosis method based on dual signal fusion and back propagation neural network is proposed. The integrated modeling of motor gear transmission system is carried out, and the co-simulation of motor gear transmission system is carried out. By simulating different gear faults, the fault signals of stator current and electromagnetic torque of motor side are obtained. The gear fault frequency signals are analyzed by double-tree complex wavelet transform, fault characteristics are extracted, and a rich gear fault sample database is established. The back propagation neural network is built and an improved adaptive learning rate algorithm is proposed to accurately classify gear broken teeth and wear faults. In order to verify the effectiveness of the proposed method, a gear fault experimental platform is built to diagnose the corresponding gear faults. The results show that the proposed method can accurately identify gear fault types under different speed and load torque conditions. Compared with only using a signal of stator current and electromagnetic torque for gear fault diagnosis, the proposed method has a higher accuracy.