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

Liang Xi (), Wei Wang, Jingyi Chen and Xuefeng Wu
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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

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 7, No 14, 3259-3272

Abstract: 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.

Keywords: Tool condition monitoring; Multivariate time series; Feature extraction; Attention mechanism; Convolutional neural networks (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02202-4

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