基于EEMD-SVD-LTSA的高速列车蛇行演变特征提取框架
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U216.3;TH17;TN98

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国家自然科学基金(51475387)、中央高校基本业务费专项基金(2682014CX033)、四川省科技创新苗子工程(2015102)项目资助


High-speed train small hunting evolution feature extraction based on EEMD-SVD-LTSA framework
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    摘要:

    高速列车一旦出现蛇行失稳,列车的运行安全会受到严重威胁。在出现蛇行失稳前,高速列车会进入小幅蛇行发散状态,因此监测列车小幅蛇行演变趋势可以预测列车的运行状况,然而现有的文献鲜有对小幅蛇行演变特征进行研究,为此,提出一种基于EEMD-SVD-LTSA的高速列车特征提取框架,识别其演变趋势是小幅发散还是小幅收敛,进而预测列车运行状况。通过在线实验数据验证表明,提出的框架能成功提取高速列车小幅收敛、小幅发散的运行特征,且使用LSSVM的识别率达到100%,从而及时预测高速列车的运行状态,保障列车的运行安全。

    Abstract:

    Once the high-speed train is unstable, the operation safety of the train will be seriously threatened. Before the high-speed train appears to be unstable, it will enter a small hunting divergence state. Therefore, monitoring the train′s evolutionary trend of small hunting can predict the running status of trains. However, the existing literature rarely studies the evolution characteristics of small hunting, therefore, this paper proposes a high-speed train feature extraction framework based on EEMD-SVD-LTSA method, to identify whether the evolution trend is small divergence or small convergence, and then predict the train running status. The verification of online experimental data shows that the framework proposed in this paper can successfully extract the small convergence and small divergence operating characteristics of high-speed trains, and the recognition rate of using LSSVM can reach 100%, so as to predict the running status of high-speed trains in time and ensure the safety of trains.

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冉伟,宁静,陈杨,陈春俊.基于EEMD-SVD-LTSA的高速列车蛇行演变特征提取框架[J].电子测量技术,2019,42(5):1-5

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  • 在线发布日期: 2021-07-29
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