期刊
IEEE ACCESS
卷 8, 期 -, 页码 1975-1985出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2962734
关键词
Rotating machinery; convolutional neural network; fault diagnosis; activation function
资金
- National Natural Science Foundation of China [51875375, 51875376]
- China Postdoctoral Science Foundation [2019T120456]
- Suzhou Prospective Research Program [SYG201802]
Rotating machinery has been developed with high complexity and precision, and bearings and gears are crucial components in the machinery system. Deep learning has attracted considerable attention from researchers in this area. The convolutional neural network (CNN) is a typical deep learning model that has a strong capability for automatically extracting features from raw data. This capability minimizes dependence on expert knowledge during feature extraction and selection. In CNN, hyperparameters, such as activation functions, can directly influence the performance of the model. In this study, the improved rectified linear units (ReLU)-CNNs are proposed for machinery fault diagnosis. The model's input are raw vibration signals without feature extraction and selection. It is experimentally validated for fault diagnosis using bearing and gearbox datasets. Results show that the proposed method can obtain satisfactory accuracy with enhanced convergence speed. For both datasets, the proposed method gives better diagnosis accrues than the other compared models. The proposed model can take advantages of standard ReLU-CNN, and these advantages can overcome traditional activation functions' vanishing gradient problems. Meanwhile, the improved ReLU-CNN has a new property that makes it perform better than the standard ReLU-CNN.
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