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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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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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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DOI: https://doi.org/10.1007/s13042-023-01830-9