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A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case

Author

Listed:
  • Jiayang Liu

    (Wuhan University)

  • Fuqi Xie

    (Wuhan University)

  • Qiang Zhang

    (Wuhan University)

  • Qiucheng Lyu

    (Wuhan University)

  • Xiaosun Wang

    (Wuhan University)

  • Shijing Wu

    (Wuhan University
    Wuhan University)

Abstract

Mechanical system fault diagnosis is essential to save costs and ensure safety. Generally, rotating machinery operates in nonstationary cases, which makes fault features complex and difficult to identify. However, existing fault diagnosis methods have the following limitations. (1) Consider only time or frequency domain features fusion. (2) Extract the fusion features representation only in the Euclidean domain. Based on that, a novel method based on multisensory time-frequency features fusion and graph attention network is proposed for rotating machinery fault diagnosis under the nonstationary case. First, the multi-sensor time series are converted into multi-sensor time-frequency maps by image-to-matrix, matrix concatenation, and matrix-to-image operations. Then, simple linear iterative clustering is applied to make the superpixels in the multi-sensor time-frequency maps into nodes and form graphs based on color and texture features. Finally, the graph attention network with residual connection is applied to distinguish the rotating machinery’s health status. The proposed method is verified using gearbox test rig data and bearing public data, respectively. The experimental results indicate that the proposed method can provide more reliable and accurate fault diagnosis results for rotating machinery than other methods.

Suggested Citation

  • Jiayang Liu & Fuqi Xie & Qiang Zhang & Qiucheng Lyu & Xiaosun Wang & Shijing Wu, 2024. "A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3197-3217, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02198-x
    DOI: 10.1007/s10845-023-02198-x
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    References listed on IDEAS

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    1. Jiyoung Song & Young Chul Lee & Jeongsu Lee, 2023. "Deep generative model with time series-image encoding for manufacturing fault detection in die casting process," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3001-3014, October.
    2. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
    3. Kaibo Zhou & Chaoying Yang & Jie Liu & Qi Xu, 2023. "Deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1965-1974, April.
    4. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
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