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Harmonic reducer in-situ fault diagnosis for industrial robots based on deep learning

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Abstract

The harmonic reducer is an essential kinetic transmission component in the industrial robots. It is easy to be fatigued and resulted in physical malfunction after a long period of operation. Therefore, an accurate in-situ fault diagnosis for the harmonic reducers in an industrial robot is especially important. This paper proposes a fault diagnosis method based on deep learning for the harmonic reducer of industrial robots via consecutive time-domain vibration signals. Considering the sampling signals from industrial robots are long, narrow, and channel-independent, this method combined a 1-dimensional convolutional neural network with matrix kernels (1-D MCNN) adaptive model. By adjusting the size of the convolution kernels, it can concentrate on the contextual feature extraction of consecutive time-domain data while retaining the ability to process the multi-channel fusion data. The proposed method is examined on a physical industrial robot platform, which has achieved a prediction accuracy of 99%. Its performance is appeared to be superior in comparison to the traditional 2-dimensional CNN, deep sparse automatic encoding network (DSAE), multilayer perceptual network (MLP), and support vector machine (SVM).

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Xing Zhou, HuiCheng Zhou, YiMing He, ShiFeng Huang, ZhiHong Zhu & JiHong Chen

  2. Foshan Institute of Intelligent Equipment Technology, Foshan, 528234, China

    ShiFeng Huang

Authors
  1. Xing Zhou
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  2. HuiCheng Zhou
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  3. YiMing He
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  4. ShiFeng Huang
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  5. ZhiHong Zhu
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  6. JiHong Chen
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Corresponding author

Correspondence to JiHong Chen.

Additional information

This work was supported by the Basic and Applied Basic Research Fund of Guangdong Province (Grant No. 2020B1515120010).

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Zhou, X., Zhou, H., He, Y. et al. Harmonic reducer in-situ fault diagnosis for industrial robots based on deep learning. Sci. China Technol. Sci. 65, 2116–2126 (2022). https://doi.org/10.1007/s11431-022-2129-9

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  • DOI: https://doi.org/10.1007/s11431-022-2129-9

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