基于一种改进的一维卷积神经网络电机故障诊断方法

Fault diagnosis is essential to ensure proper operation of the motor. Convolutional neural network (CNN) has showed a better performance on diagnosing single motor faults. However, traditional CNN has limitations in dealing with different sizes of data. To solve this problem, a fault diagnosis method was proposed based on spatial pyramid pooling (SPP), one-dimensional convolutional neural network and a parameter optimization strategy. The method was arranged to make not only the network be possible to process different sizes of data, but also reduce the complexity of the network structure and the amount of computation required. The parameter optimization strategy was designed to solve the scale mismatch problem in pyramid pooling during the diagnosis process. The simulation results show that, compared with the traditional network, the proposed method can improve the robustness and generalization ability of the network structure, making the fault diagnosis more quickly and accurately for motor.