| Citation: | CHE Changchang, WANG Huawei, NI Xiaomei, et al. Fault diagnosis of rolling bearing based on deep residual shrinkage network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(7): 1399-1406. doi: 10.13700/j.bh.1001-5965.2020.0194(in Chinese)
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Accurate fault diagnosis of rolling bearing is a necessary means to ensure the safe and reliable operation of mechanical equipment. In this paper, a fault diagnosis method based on Deep Residual Shrinkage Network (DRSN) is proposed for the vibration signal of rolling bearing with multiple faults and long time series. Firstly, fault samples are constructed according to the collected rolling bearing data. For the vibration signals of long time series under various fault types, the long time series are matrixed according to a certain size, so as to form the gray image fault samples of multiple fault types. Aimed at the performance degradation process of rolling bearings from normal to fault, the whole life cycle fault samples are constructed for fault diagnosis through random sampling of multiple sampling points. Secondly, based on the multi-layer deep learning model, the residual shrinkage network module is added to the Convolutional Neural Network (CNN) to build the deep residual shrinkage network model, in which the model degradation problem of the multi-layer network model is solved by adding the residual term to the network training, and the sample noise reduction is realized by using soft thresholding. Finally, in order to verify the effectiveness of the proposed method, multi-fault time series samples and life cycle fault samples of rolling bearing are collected for fault diagnosis. The result of the example shows that the proposed model has good robustness under the noise interference, there is no obvious network degradation under the multi-layer network model, and it can maintain a high accuracy of fault diagnosis. When dealing with two kinds of bearing fault datasets, compared with other models, this method has lower training error, and the average accuracy of fault classification is increased by 1%-6%.
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