关键词: 齿轮故障, 深度学习, 特征提取, 支持向量机, 智能诊断

Abstract: For the fault diagnosis of gears, using statistical methods for feature extraction, deep learning neural network (DNN), particle swarm optimization algorithm and SVM technology, a novel intelligent diagnosis model was proposed, which combined the deep learning feature extraction and particle swarm optimization SVM state identification. The model combined the frequency domain features which were extracted by deep learning adaptively and the time domain features which were extracted by mathematical statistics method to form a combined feature vectors, then using particle swarm optimization SVM to diagnose the feature vectors. This model provides reliable identification of intermediate shaft gears of different types of faults in the fault diagnoses of gear transmission system test bench, and the satisfactory diagnostic results are obtained. The application results also verify the effectiveness of the adaptive extraction of spectral features based on deep learning.

Key words: gear fault, deep learning, feature extraction, support vector machine(SVM), intelligent diagnosis