Research on Bearing Fault Diagnosis Method based on Attention Entropy-COA-SVM

DOI

10.2991/978-94-6463-845-5_104How to use a DOI?

Keywords

Attention entropy; Support vector machine; Long-nose raccoon optimization algorithm; Fault diagnosis

Abstract

Aiming at the problem of bearing fault diagnosis, a fault diagnosis method based on Attention Entropy (AE) and Support Vector Machines (SVM) optimized by Coati Optimization Algorithm (COA) is proposed. Firstly, the attention entropy feature of bearing vibration signal is extracted, which can extract the key information in the signal more effectively, so as to enhance the identifiability of fault features and improve the accuracy of fault identification. Secondly, the attention entropy feature is input into the COA-SVM model for bearing fault classification. Through the verification on the fault data set of Case Western Reserve University, the results show that the method can effectively identify different fault types. Through comparative experiments, the method based on attention entropy has obvious advantages in fault detection and classification performance, and the accuracy of this method can reach 94.64%.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bing Liu
AU  - Yanshuai Yang
AU  - Zhentao Mi
AU  - Ruifeng Zhang
AU  - Yuxi Song
AU  - Guoqing Zhu
PY  - 2025
DA  - 2025/09/16
TI  - Research on Bearing Fault Diagnosis Method based on Attention Entropy-COA-SVM
BT  - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025)
PB  - Atlantis Press
SP  - 1063
EP  - 1070
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-845-5_104
DO  - 10.2991/978-94-6463-845-5_104
ID  - Liu2025
ER  -