Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

• Published in: Chinese journal of aeronautics Vol. 35; no. 10; pp. 301 - 312
• Main Authors: ZHANG, Jiqiang, KONG, Xiangwei, LI, Xueyi, HU, Zhiyong, CHENG, Liu, YU, Mingzhu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022; Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment,Northeastern University,Shenyang 110819,China%Angang Steel Company Limited,Anshan 114021,China; School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China%School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China; Key Laboratory of Vibration and Control of Aero-Propulsion System,Ministry of Education,Northeastern University,Shenyang 110819,China
• Subjects: Batch normalization; Convolutional neural network; Fault diagnosis; Similarity pruning; Spatial dropout; Fault diagnosis; Spatial dropout; Convolutional neural network; Batch normalization; Similarity pruning; Convolutional neural net-work
• ISSN: 1000-9361
• DOI: 10.1016/j.cja.2022.03.007

Bearing pitting, one of the common faults in mechanical systems, is a research hotspot in both academia and industry. Traditional fault diagnosis methods for bearings are based on manual experience with low diagnostic efficiency. This study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization. Deep separable convolution extracts features from the raw bearing vibration signals, during which a 3 × 1 convolutional kernel with a one-step size selects effective features by adjusting its weights. The similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller weights. The spatial dropout regularization method focuses on bearing signal fault features, improving the independence between the bearing signal features and enhancing the robustness of the model. A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability improvement. To validate the effectiveness of the proposed method, we collect raw vibration signals from bearings in eight different health states. The experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.