基于PCA和TCN-Attention的重载铁路钢轨剥离伤损退化趋势预测
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U239.4

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国家重点研发计划(2021YFF05011);湖南省教育厅青年项目(22B0586) ;湖南省教育厅项目(2022JGYB186)


Prediction of stripping damage and degradation trend of heavy-haul railway rails based on PCA and TCN-Attention
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    摘要:

    重载铁路在高强度的运输过程中极易导致钢轨产生剥离、磨耗等伤损影响行车安全,为了保证铁路的安全运行,对钢轨的伤损状态监测和预测是非常重要的。然而,目前钢轨伤损检测方法主要以人工道路巡检为主,检测结果存在主观性强、伤损程度量化难、伤损退化趋势预测难等问题。针对现有问题,本文提出了一种基于PCA和TCN-Attention的重载铁路钢轨剥离伤损退化趋势预测新方法。首先,从钢轨剥离伤损振动信号中提取时域、频域特征,并采用PCA对提取到的高维特征进行降维;其次,利用时序样本间特征的差异性,构建出钢轨剥离伤损退化指标描述退化趋势性,解决伤损状态度量难的问题;利用TCN网络模型结合Attention机制对有效特征的关注提升模型的预测精度;最后,利用某铁路机务段采集的钢轨从正常到出现损伤直至失效的全生命周期振动数据,对本文所提方法的有效性进行验证,实验结果表明本文所提出的方法能准确地预测钢轨剥离伤损的退化趋势。

    Abstract:

    In the process of high-intensity transportation, heavy-haul railways are prone to damage such as stripping and abrasion of rails, which affects driving safety. In order to ensure the safe operation of the railway, it is very important to monitor the damage status of the rail. However, at present, the rail damage detection method is mainly based on manual road inspection, and the detection results have problems such as strong subjectivity, difficulty in quantifying the degree of damage, and difficulty in predicting the trend of damage degradation. In order to solve the existing problems, this paper proposes a new method based on PCA and TCN-Attention to predict the degradation trend of stripping damage of heavy-haul railway rails. Firstly, the time-domain and frequency-domain features were extracted from the rail stripping damage vibration signal, and the high-dimensional features were reduced by PCA. Secondly, by using the difference of characteristics between time series samples, the degradation index of rail stripping damage was constructed to describe the degradation trend, which solved the problem of difficulty in measuring the damage state. The TCN network model combined with the attention mechanism was used to pay attention to the effective features to improve the prediction accuracy of the model. Finally, the effectiveness of the proposed method is verified by using the vibration data of the whole life cycle of the rail collected by a railway locomotive depot from normal to damage to failure, and the experimental results show that the proposed method can accurately predict the degradation trend of rail stripping damage.

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王忠美,吴海波,刘建华,等. 基于PCA和TCN-Attention的重载铁路钢轨剥离伤损退化趋势预测[J]. 科学技术与工程, 2024, 24(28): 12333-12341.
Wang Zhongmei, Wu Haibo, Liu Jianhua, et al. Prediction of stripping damage and degradation trend of heavy-haul railway rails based on PCA and TCN-Attention[J]. Science Technology and Engineering,2024,24(28):12333-12341.

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  • 收稿日期:2024-01-10
  • 最后修改日期:2024-10-10
  • 录用日期:2024-03-21
  • 在线发布日期: 2024-11-05
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