| Citation: | HU Aijun, SUN Junhao, XING Lei, et al. Intelligent fault diagnosis of rotating machinery based on impact feature extraction[J]. Journal of Aerospace Power, 2023, 38（12）：2973-2981 doi: 10.13224/j.cnki.jasp.20220106
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In view of the gear and bearing faults, an intelligent fault diagnosis model of rotating machinery based on impact feature extraction capsule network was proposed. Based on the structure of the capsule network, the original fault vibration signal was taken as the input, and the first wavelet kernel convolution layer was constructed to extract the impact fault features, so as to improve the interpretability of the feature extraction of the deep learning network. After the wavelet kernel convolution layer, a convolution layer was extended to strengthen the features extracted by the first wavelet kernel convolution layer. The enhanced features were processed through the primary and digital capsule layers to output the final diagnosis result. Therefore, an “end-to-end” wavelet convolution capsule network model was constructed. The impact feature extraction ability of the proposed model was verified through the feature visualization analysis for each layer. The datasets verification results of three different experimental platforms show that the recognition accuracy of gears and bearings with different fault types and fault degrees can reach 100% at most, presenting good generalization ability.
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