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An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

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Abstract

Rotating machinery is a very important mechanical device widely used in critical industrial applications. Efficient fault detection and diagnosis are key challenges in the maintenance and operational reliability of rotating machinery. To overcome this problem, a novel fault diagnosis method for rotating machinery based on deep residual neural network (DRNN) and data fusion is proposed. First, the time domain and frequency domain features of the original signal are extracted through the Short-time Fourier transform (STFT) layer, and then the deep residual network and the fusion embedding layer are used to fuse the time domain, frequency domain and spatial domain features to obtain high-quality low-dimensional fusion features. Finally, the fault type is obtained through the classifier. The proposed method is applied to the fault diagnosis of rolling bearing and gearbox, and the performance of the model has been tested comprehensively, including model training test, anti-noise test, fault tolerance test. The results confirm that the proposed method is much more effective and robust for feature learning, model training, anti-noise, fault tolerance and fault diagnosis than other fusion learning methods and single sensor-based methods. This fully reflects the advantages of multi-source information fusion in ensuring the reliable operation of rotating machinery.

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Acknowledgements

This project was supported by the Natural Science Foundation of Heilongjiang Province, China (Grant NO. E2017023); and the Foundation of Science and Technology on Reactor System Design Technology Laboratory (HT-KFKT-14-2017003).

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Authors and Affiliations

  1. Key Laboratory of Nuclear Safety and Advanced Nuclear Energy Technology, Ministry of Industry and Information Technology, Harbin, China

    Binsen Peng, Hong Xia, M. Annor-Nyarko, Shaomin Zhu, Yongkuo Liu & Jiyu Zhang

  2. Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin, 150001, China

    Binsen Peng, Hong Xia, M. Annor-Nyarko, Shaomin Zhu, Yongkuo Liu & Jiyu Zhang

  3. Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, China

    Xinzhi Lv

  4. Nuclear Installations Directorate, Nuclear Regulatory Authority, Accra, Ghana

    M. Annor-Nyarko

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  1. Binsen Peng
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  2. Hong Xia
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  3. Xinzhi Lv
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  4. M. Annor-Nyarko
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  5. Shaomin Zhu
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Correspondence to Hong Xia.

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Peng, B., Xia, H., Lv, X. et al. An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network. Appl Intell 52, 3051–3065 (2022). https://doi.org/10.1007/s10489-021-02555-4

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