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A grouping-attention convolutional neural network for performance degradation estimation of high-speed train lateral damper

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Abstract

The performance degradation of lateral damper has a direct impact on the operation of high-speed trains. Existing studies pay more attention to the fault diagnosis of lateral dampers, but the researches on the performance degradation estimation are not comprehensive. In this paper, a novel Grouping-Attention Convolutional Neural Network is proposed to estimate the performance degradation of lateral damper by regression. The proposed structure processes high-speed train vibration signals and fully considers the multi-channel characteristic of the signals. Specifically, the proposed structure consists of a Grouping-Attention part and an Output part. Before high-speed train vibration signals are input into Grouping-Attention part, the signals from different channels are first grouped according to frequency similarity. The Grouping-Attention part contains a Grouping part and an Attention Merging part. The Grouping part extracts features from the grouped signals in parallel. The Attention Merging part applies attention mechanism to merge features from each group. The Output part decodes the features obtained in Grouping-Attention part and outputs the estimation results. The effectiveness and superiority of the proposed structure are verified at 200 km/h and 300 km/h operating speeds.

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Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, 999 Xi’an Road, Chengdu, 611756, Sichuan, China

    Junxiao Ren, Weidong Jin, Yunpu Wu & Zhang Sun

  2. China-ASEAN International Joint Laboratory of Integrated Transportation, Nanning University, 8 Longting Road, Nanning, 541699, Guangxi, China

    Weidong Jin

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  1. Junxiao Ren
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Ren, J., Jin, W., Wu, Y. et al. A grouping-attention convolutional neural network for performance degradation estimation of high-speed train lateral damper. Appl Intell 53, 658–682 (2023). https://doi.org/10.1007/s10489-022-03368-9

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