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AANet: adaptive attention network for rolling bearing fault diagnosis under varying loads

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Abstract

Recently, modern intelligent fault diagnosis algorithms based on deep learning have been widely used to recognize the health state of rolling bearings. However, the constantly varying load in real industry leads to unsatisfactory diagnosis results. How to make the models effectively diagnose the health state of rolling bearings under varying loads is a key issue. In this paper, an Adaptive Attention Network (AANet) is proposed to resolve the issue. That the interference is introduced by the Multi-scale Convolution Module with wide kernels (MCM) at the head of the AANet is the premise for extending the model to other loads. And the Adaptive Attention Modules (AAMs) embedded in the AANet distinguishe state-related features and unrelated features, which enhances the diagnostic ability of the model across loads. In order to verify the effectiveness of the algorithm, experiments have been performed on a public data set. Experimental results show that the average accuracy of this algorithm achieves 0.976, which can effectively recognize the health state of rolling bearings under varying loads, compared to other algorithms.

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Data availability

The data that support the findings of this study are openly available at the following URL: https://engineering.case.edu/bearingdatacenter/download-data-file.

Abbreviations

MCM:

Multi-scale Convolutional Module with wide kernels

CAM:

Channel Attention Module

LAM:

Length Attention Module

AAM:

Adaptive Attention Module including channel-first mode, length-first mode, parallel mode and fusion mode

ResBlock:

Residual Block

ResNet:

Residual Network

AANet:

Adaptive attention network

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Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDC04000000), the National Natural Science Foundation of China (Grant No. 61703393), the Natural Science Foundation of Liaoning Province (Grant No. 2019-MS-343 and No. 20180520008), the Liao-Ning Revitalization Talents Program (Grant No. XLYC2002055) and the K.C.Wong Education Foundation.

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Authors and Affiliations

  1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Shixin Sun, Jie Gao, Wei Wang, Jinsong Du & Xu Yang

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Shixin Sun, Jie Gao, Wei Wang, Jinsong Du & Xu Yang

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Shixin Sun & Wei Wang

  4. Key Laboratory On Intelligent Detection and Equipment Technology of Liaoning Province, Shenyang, 110179, China

    Shixin Sun, Jie Gao, Wei Wang, Jinsong Du & Xu Yang

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  1. Shixin Sun
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  2. Jie Gao
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Correspondence to Shixin Sun.

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Sun, S., Gao, J., Wang, W. et al. AANet: adaptive attention network for rolling bearing fault diagnosis under varying loads. Int. J. Mach. Learn. & Cyber. 14, 3227–3241 (2023). https://doi.org/10.1007/s13042-023-01830-9

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