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Appending-inspired multivariate time series association fusion for tool condition monitoring

Author

Listed:
  • Liang Xi

    (Harbin University of Science and Technology)

  • Wei Wang

    (Harbin University of Science and Technology)

  • Jingyi Chen

    (Harbin University of Science and Technology)

  • Xuefeng Wu

    (Harbin University of Science and Technology)

Abstract

In intelligent machining, tool condition monitoring (TCM) is crucial to improving tool efficiency and machining accuracy, which requires the real-time analysis and feature extraction of multivariate time series signals collected by multiple sensors. However, multivariate time series are ultra-high-dimensional and difficult to perform representation learning directly, requiring sampling and typical feature extraction. The existing deep feature extractors based on Sequential sampling, Random sampling, or Window sampling, are poor at capturing the critical information from the huge amount of time series data, and ignore the temporal associations, so the actual results are not satisfactory in terms of prediction accuracy and efficiency. Therefore, we propose an appending-inspired multivariate time series association fusion method for TCM tasks: after the necessary denoising, we capture typical time-domain, frequency-domain, and time-frequency-domain features of multivariate time series based on the proposed appending-inspired feature capturer to fully consider the temporal associations, and employ the ACNNs (Attention-based Convolutional Neural Networks) to extract and fuse the multivariate time series features for real-time TCM tasks. The experimental results on NASA and PHM2010 datasets show that our method can real-time and effectively monitor the tool condition and accurately predict the tool wear state.

Suggested Citation

  • Liang Xi & Wei Wang & Jingyi Chen & Xuefeng Wu, 2024. "Appending-inspired multivariate time series association fusion for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3259-3272, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02202-4
    DOI: 10.1007/s10845-023-02202-4
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    References listed on IDEAS

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    1. Xianli Liu & Bowen Zhang & Xuebing Li & Shaoyang Liu & Caixu Yue & Steven Y. Liang, 2023. "An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 885-902, February.
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