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Parallel learning attention-guided CNN for signal denoising and mechanical fault diagnosis

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Rotating components of mechanical drive systems usually work in harsh environments, and the collected vibration signals are immensely affected by strong noise, which poses a great challenge for fault diagnosis. Intelligent fault diagnosis techniques have been widely used in the field of mechanical fault diagnosis, but the denoising process and diagnosis process of most intelligent schemes are independent of each other, ignoring the synergy of both. In this paper, a parallel learning attention-guided convolutional neural network (PLA-CNN) combining noise reduction and fault diagnosis for mechanical fault detection is presented, which can realize the cooperation and mutual learning between networks for improving the ability of deep learning. The PLA-CNN consists of a global feature sharing network and two special task networks with feature attention modules. The performance of the proposed network is evaluated using datasets of gear and bearing defects. The results show that the signal denoising network can improve the signal-to-noise ratio of the input signal, and the fault diagnosis network can improve the fault identification accuracy with the assistance of the former. The method has a better performance compared with traditional intelligent fault diagnosis methods.

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Acknowledgements

The authors would like to thank the reviewers for the careful reading and constructive feedback on the material presented in this article.

Funding

This work is supported by the Science Foundation of Hubei Key Laboratory of hydropower equipment design and maintenance (No. 2020KJX11).

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Authors and Affiliations

  1. College of Mechanical and Power Engineering, China Three Gorges University, Yichang, 443002, People’s Republic of China

    Xianyou Zhong, Yafei Li & Tianyi Xia

  2. Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang, 443002, People’s Republic of China

    Xianyou Zhong, Yafei Li & Tianyi Xia

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  1. Xianyou Zhong
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  2. Yafei Li
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Correspondence to Xianyou Zhong.

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Zhong, X., Li, Y. & Xia, T. Parallel learning attention-guided CNN for signal denoising and mechanical fault diagnosis. J Braz. Soc. Mech. Sci. Eng. 45, 239 (2023). https://doi.org/10.1007/s40430-023-04139-4

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  • DOI: https://doi.org/10.1007/s40430-023-04139-4

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