PSO优化深度神经网络诊断齿轮早期点蚀故障

When gear faults were diagnosed based on the data-driven method, feature extraction was generally performed by Fourier transform, etc. The feature extraction method used has a great influence on the diagnosis results. Therefore, deep neural network(DNN) was proposed to diagnose early gear pitting faults and the vibration signals are directly used as the network inputs to avoid errors caused by feature extraction. In addition, the particle swarm optimization(PSO) algorithm was applied to optimize the DNN for obtaining a more stable training process and better diagnosis results. Principal component analysis(PCA) algorithm was used to reduce the dimensions of the DNN outputs. The data collected from the experiment was used to train and test the DNN. The fault diagnostic accuracy can reach over 90%, which proves that the proposed method is reasonably effective.