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Dynamic spatial–temporal graph-driven machine remaining useful life prediction method using graph data augmentation

Author

Listed:
  • Chaoying Yang

    (Huazhong University of Science and Technology)

  • Jie Liu

    (Huazhong University of Science and Technology)

  • Kaibo Zhou

    (Huazhong University of Science and Technology)

  • Xinyu Li

    (Huazhong University of Science and Technology)

Abstract

It is beneficial to maintain the normal operation of machines by conducting remaining useful life (RUL) prediction. Recently, graph data-driven machine RUL prediction methods have made a great success, since graph can model spatial and temporal dependencies of signals. However, the constructed graphs still have some limitations: (1) In the practical industrial production, the installation of multi-sensor networks is expensive and hard to achieve, so the single sensor is commonly used for data monitoring. However, most of these methods constructed graphs by establishing relationships between the different sensors, which are completely unsuitable for prediction tasks in single-sensor scenarios. (2) The quality of constructed graph is low, where the graph structure is fixed, failing in representing the machine degradation process. To overcome these limitations, a dynamic spatial–temporal (ST) graph-driven machine RUL prediction method using graph data augmentation (GDA) is proposed. The ST graph is constructed using short-time Fourier transform, capturing the frequency-domain and time-domain information hidden in the signals. Then, a GDA framework is designed to generate dynamic ST graphs, enlarging the structural differences of subgraphs. Subsequently, a GDA-based graph deep learning prediction model is constructed for dynamic ST graph-based RUL prediction, where an autoencoder-based graph embedding module is designed to replace simple Readout. Verification experiments are conducted on two case studies, and the results show that the proposed prediction method achieves a competitive performance.

Suggested Citation

  • Chaoying Yang & Jie Liu & Kaibo Zhou & Xinyu Li, 2024. "Dynamic spatial–temporal graph-driven machine remaining useful life prediction method using graph data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 355-366, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02052-6
    DOI: 10.1007/s10845-022-02052-6
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    References listed on IDEAS

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    1. Pradeep Kundu & Seema Chopra & Bhupesh K. Lad, 2019. "Multiple failure behaviors identification and remaining useful life prediction of ball bearings," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1795-1807, April.
    2. Tae San Kim & So Young Sohn, 2021. "Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2169-2179, December.
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