PSO优化深度神经网络诊断齿轮早期点蚀故障

基于数据驱动方法诊断齿轮故障时一般会用傅里叶变换等进行特征提取，特征提取方法的选取对诊断结果影响很大.提出应用深度神经网络来诊断齿轮早期点蚀故障，直接以采集的振动信号作为网络输入，可以避免特征提取环节产生误差.此外，应用粒子群算法优化深度神经网络，使训练过程更稳定、诊断率更高.在分析结果时应用主成分分析法对网络输出进行降维.用实验采集的数据训练并测试网络，诊断正确率能达到90%之上，证明所提出的方法是合理、可用的.

Diagnosis of Gear Early Pitting Faults Using PSO Optimized Deep Neural Network

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.