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Rolling bearing fault diagnosis based on Dropout-multi-scale dilated convolution neural network

Published:2023-07-18 author:CHEN Wei, WANG Fu-song, GUO Jing, et al. Browse: 418 Check PDF documents

Rolling bearing fault diagnosis based on Dropout-multi-scale dilated

convolution neural network

CHEN Wei1, WANG Fu-song2, GUO Jing2, HUANG Bo-hao2, BAI Yi-shuo2

(1.China Coal Information Technology (Beijing) Co., Ltd., Beijing 100029, China;

2.School of Mechanical

Electronic ＆ Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

Abstract: In order to improve the feature extraction ability of the fault diagnosis model to the low signal-to-noise ratio signal of the fault bearing, and make the model still play a role in the strong noise environment,a rolling bearing fault diagnosis model based on Dropout-multi-scale dilated convolutional neural networks (D-MDCNN)was proposed. Firstly, the Dropout data preprocessing was used to "damage" the training data, which was forced the model to diagnose faults by relying only on a few features, so as to improve the anti-noise ability of the model.Then, multi-scale information expansion was completed by using the dilated convolution with different dilation rates, and feature extraction and fault diagnosis were completed by using CNN module. At the same time,batch normalization was added to the model to accelerate the convergence speed of model training and improve the performance of the model. Finally, the model was verified by the bearing data set of Case Western Reserve University (CWRU) and the gearbox data set of Southeast University (SU), and the experimental results were compared with those of other deep learning models. The experiment results show that D-MDCNN can achieve 99% diagnostic accuracy on the bearing data set of CWRU and the gearbox data set of SU in noise free to 4dB noise environments, and has higher diagnostic accuracy and anti-noise ability than other models. The results show that the model based on D-MDCNN is an effective bearing fault diagnosis model.

Key words: strong noise environment; low signal-to-noise ratio signal; fault diagnosis of rolling bearing; fault feature extraction; Dropout-multi-scale dilated convolutional neural networks (D-MDCNN); damage the training data; anti-noise ability

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