Improved Interval Prediction of Small-Amplitude Hunting of High-Speed Trains

Ning, Jing, Fang, Mingkuan, Wang, Duoying, Chen, Chunjun and Ouyang, Huajiang ORCID: 0000-0003-0312-0326 (2023) Improved Interval Prediction of Small-Amplitude Hunting of High-Speed Trains. IEEE Transactions on Instrumentation and Measurement, 72. pp. 1-11.

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Official URL: http://dx.doi.org/10.1109/tim.2023.3287261

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	High-speed trains, interval prediction, lower upper bound estimation (LUBE), neural network, small-amplitude hunting instability
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	18 Jul 2023 08:45
Last Modified:	17 Aug 2023 09:31
DOI:	10.1109/tim.2023.3287261
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3171709