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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 9/2023

28-04-2023 | Original Article

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

Authors: Shixin Sun, Jie Gao, Wei Wang, Jinsong Du, Xu Yang

Published in: International Journal of Machine Learning and Cybernetics | Issue 9/2023

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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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Metadata
Title
AANet: adaptive attention network for rolling bearing fault diagnosis under varying loads
Authors
Shixin Sun
Jie Gao
Wei Wang
Jinsong Du
Xu Yang
Publication date
28-04-2023
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 9/2023
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01830-9

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