Abstract:Aimed at the problem of low accuracy of multi-fault signal diagnosis of gearbox under complex working conditions, a gearbox fault diagnosis method based on hybrid features and stacked sparse auto-encoder was proposed. The singular value feature and the sample entropy feature decomposed by wavelet were extracted from the perspective of microscopic signal feature. The time-domain features of the fault signal were extracted from the macroperspective, and the three features were fused and input into the deep neural network obtained by sparse auto-encoder and Softmax stack for feature optimization and classification recognition. The experimental results show that the diagnosis of six gear box fault data shows high classification and recognition accuracy under two different working conditions, the comprehensive performance of the constructed classification model is higher than that of other comparison models, so the proposed method can effectively diagnose the gearbox fault.