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Remaining useful life prediction of rolling bearings based on parallel feature extraction

Chao Li (Department of Automation, University of Science and Technology of China, Hefei, China)
Weimin Zhai (Department of Automation, University of Science and Technology of China, Hefei, China)
Weiming Fu (Department of Automation, University of Science and Technology of China, Hefei, China)
Jiahu Qin (Department of Automation, University of Science and Technology of China, Hefei, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China)
Yu Kang (Department of Automation, University of Science and Technology of China, Hefei, China)

Robotic Intelligence and Automation

ISSN: 2754-6969

Article publication date: 28 November 2024

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Abstract

Purpose

This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge and removes redundant handcrafted feature information, additionally, which focuses on the important features at different time scales.

Design/methodology/approach

Distinct from traditional parallel feature extraction methods, which can lead to information redundancy, a one-dimensional convolutional autoencoder is introduced to process selected indicators to remove redundancy and retain useful feature information. To fully capture the important degradation information within different stages in the feature sequences, a novel multi-scale attention feature fusion module is proposed to extract degradation features at different time scales. Considering the impact of degradation modes on RUL prediction, a dual-task prediction module based on no degradation mode labels is designed to obtain accurate RUL.

Findings

Comparative experiments and ablation studies on the PHM2012 bearing dataset verified the effectiveness of the proposed method. Furthermore, the rationality of the selected parameters is confirmed through model parameter analysis.

Originality/value

The novelty of the proposed method is that it not only provides prior knowledge but also further removes redundant information from prior knowledge. In addition, the distribution differences between the original features and their multi-scale convolution results are measured through Kullback–Leibler divergence as the attention scores, which allows the proposed method to focus on important information at different time scales.

Keywords

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant U23A20323 and Grant 62203418; and in part by USTC Research Funds of the Double First-Class Initiative under Grant YD2100002011.

Citation

Li, C., Zhai, W., Fu, W., Qin, J. and Kang, Y. (2024), "Remaining useful life prediction of rolling bearings based on parallel feature extraction", Robotic Intelligence and Automation, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/RIA-03-2024-0061

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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