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Fault diagnosis for rolling bearing of road heading machine via SVDS-ICNN

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Abstract

Aiming at the problems of nonstationary, nonlinear, strong background noise interference and difficult feature extraction of rolling bearing vibration signal of road heading machine, a fault diagnosis method based on improved singular value decomposition, S-transform and improved convolutional neural network (ICNN) was proposed. First, the original signal is constructed into a Hankel matrix, and the singular value decomposition of the Hankel matrix is carried out. In this paper, the singular value curvature spectrum is used to select the effective singular value for signal reconstruction, the reconstructed signal is transformed by S transformation and time–frequency transformation, and the time–frequency features are extracted. Secondly, the improved convolutional neural network takes VGG16 as the bottleneck structure and introduces multi-scale feature extraction. It also adds fine tune based on ICNN and realizes fault classification and recognition through network parameter adjustment. The method is applied to the fault diagnosis of the rolling bearing of the road heading machine, and the accuracy rate reaches 98.2%, which is 9.55% higher than that of the classic VGG16 model.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China under Grant No. 51775117 and the Innovative Project of Foshan under Grant No. 2018IT100112.

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Authors and Affiliations

  1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, People’s Republic of China

    Xiaofei Qu & Yongkang Zhang

  2. Sanyi Heavy Equipment International Holdings Limited, Shenyang, 110000, People’s Republic of China

    Li Yin

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  1. Xiaofei Qu
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  2. Yongkang Zhang
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  3. Li Yin
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Correspondence to Yongkang Zhang.

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Qu, X., Zhang, Y. & Yin, L. Fault diagnosis for rolling bearing of road heading machine via SVDS-ICNN. J Braz. Soc. Mech. Sci. Eng. 45, 439 (2023). https://doi.org/10.1007/s40430-023-04344-1

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  • DOI: https://doi.org/10.1007/s40430-023-04344-1

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