Improved Interval Prediction of Small-Amplitude Hunting of High-Speed Trains
Improved Interval Prediction of Small-Amplitude Hunting of High-Speed Trains
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DOI：
10.1109/tim.2023.3287261
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发表时间：
2023
影响因子：
5.6
通讯作者：
Jing Ning;Mingkuan Fang;Duoying Wang;Chunjun Chen;H. Ouyang
中科院分区：
文献类型：
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作者：
Jing Ning;Mingkuan Fang;Duoying Wang;Chunjun Chen;H. Ouyang
狩猎是高铁安全运行的重要因素。大多数用于监测狩猎的技术旨在检测狩猎发生情况，其中一些技术可以处理小幅度狩猎。然而，它们没有提供关于小幅度狩猎进化的信息。本工作研究小幅度狩猎的演变，目的是预测狩猎不稳定的发生。提出了一种包含多个隐层的小幅度狩猎幅度区间预测的改进方法，并通过两层模型对其进行了改进。该方法具有计算效率高、收敛速度快等优点。将该方法应用于高速列车数据，得到的预测区间的覆盖率为100%，归一化平均宽度为0.187，比现有方法预测的区间具有更高的覆盖率和更小的宽度。这一预测的置信度也很高。
Hunting is an important factor in the safe operation of high-speed trains. Most techniques used for monitoring hunting aim at detecting hunting occurrence and some of them can deal with small-amplitude hunting. However, they do not provide information about evolution of small-amplitude hunting. The present work studies the evolution of small-amplitude hunting with the goal of predicting the occurrence of hunting instability. An improved method contained multiple hidden for predicting the interval of the amplitude of small-amplitude hunting is proposed, which is improved via a two-level model. The method is computationally efficient and converges rapidly. Upon applying the proposed method to high-speed train data, the coverage probability of the resulting prediction interval (PI) is 100% and its normalized average width is 0.187, which means a higher coverage and smaller width than the interval predicted by existing methods. The confidence level of the prediction is also high.