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Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network

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Abstract

Convolutional neural network models in rolling bearing fault diagnosis have problems such as relatively large number of parameters, overly complex structure and long computation time. To address the above issues, this paper designs an improved fully connected layer (FC) layer in the multilayer perceptron model that can be used for rolling bearing fault diagnosis. Based on this idea, this paper proposes a “Reshape, Linear, Transpose, Linear” fully connected layer replacement strategy (RLTL) by using the Kronecker decomposition method. This method decomposes the weight matrix, converting one linear operation to two linear operations in the FC layer, which results in a significant reduction in the total number of parameters and calculations. On this foundation, this paper also uses one-dimensional convolution to improve the current mainstream two-dimensional lightweight convolutional neural networks and lightweight strategies, and designs nine alternative modules. The results show that the proposed RLTL module replacement scheme decreases the number of parameters of the multilayer perceptron to less than 1%, reduces the number of calculations to less than 10%, and increases the diagnostic accuracy by more than 30% in a small-sample and noise-free environment. In addition, this paper also verifies and compares the effects of different transformation methods of convolutional neural networks in each alternative module on small sample diagnosis and noise immunity diagnosis of rolling bearings.

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The figures and tables data used to support the findings of this study are included within the article, and the article permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abbreviations

CNN:

Convolutional neural network

FC:

Fully connected layer

MLP:

Multilayer perceptron

RLTL:

“Reshape, Linear, Transpose, Linear” fully connected layer

AE:

Auto-encoder

DNN:

Deep neural network

RNN:

Recurrent neural network

DBN:

Deep belief network

RBF:

Radial basis function

PNN:

Probabilistic neural network

1DCNN:

One-dimensional data convolutional neural network

2DCNN:

Two-dimensional data convolutional neural network

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Acknowledgements

This study was funded by: National Natural Science Foundation of China (Grant No. 52005352, 62173238, 52205163). Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University (VCAME202007). Open Fund of Key Laboratory of Fundamental Science for National Defense of Aeronautical Digital Manufacturing Process of Shenyang Aerospace University (SHSYS202107)

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Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang Jianzhu University, No. 25 Hunnan Middle Road, Hunnan District, Shenyang, 110168, China

    Xiaochen Zhang, Hanwen Li, Weiying Meng, Peng Zhou & Cai He

  2. Key Laboratory of Fundamental Science for National Defense of Aeronautical Digital Manufacturing Process of Shenyang Aerospace University, No. 37 Daoyi South Street, Shenbei New District, Shenyang, 110135, China

    Xiaochen Zhang

  3. The No. 95979Th Troop of PLA, Shenyang, China

    Yaofeng Liu

  4. Dongpang Colliery of Jizhong Energy Resources CO., Ltd, Xingtai, China

    Qingbo Zhao

  5. Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang, China

    Xiaochen Zhang

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  1. Xiaochen Zhang
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  2. Hanwen Li
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  3. Weiying Meng
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  7. Qingbo Zhao
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Correspondence to Weiying Meng.

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The authors declare that they have no conflicts of interest. This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal’s policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare. The data of article permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, X., Li, H., Meng, W. et al. Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network. J Braz. Soc. Mech. Sci. Eng. 44, 462 (2022). https://doi.org/10.1007/s40430-022-03759-6

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  • DOI: https://doi.org/10.1007/s40430-022-03759-6

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