SSA-LSTM high-speed railway track irregularity prediction model based on dual-stage decomposition

Chongke Wang; Xiaofeng Lu; Gaoxiang Li; Chen Wu

doi:10.1117/12.3024740

3 April 2024 SSA-LSTM high-speed railway track irregularity prediction model based on dual-stage decomposition

Chongke Wang, Xiaofeng Lu, Gaoxiang Li, Chen Wu

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 1307810 (2024) https://doi.org/10.1117/12.3024740
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

The smoothness of high-speed railway tracks is an important indicator for judging the quality of railway tracks. Accurately predicting the uneven trend of the track and dealing with it in advance is of great significance to the safe operation of high-speed railways. According to the data characteristics of specific road sections, this paper proposes a SSA-LSTM model based on dual-stage decomposition to predict track irregularity of high-speed railways. First, in order to eliminate the influence of noise in the original data, Singular Spectrum Analysis (SSA) is used to decompose, denoise, and reconstruct the original data. Secondly, in view of the existence of multiple modal mixing effects in the denoised data, the data is decomposed through the ensemble empirical mode decomposition (EEMD) method. Then, to solve the problem that the long short-term memory network (LSTM) hyper-parameters are difficult to effectively determine, the Sparrow Search Algorithm (SSA) is used to search and optimize. Finally, the optimized LSTM model is used to predict and reintegrate the decomposed multiple sequences. Experimental results show that the SSA-LSTM prediction model based on dual-stage decomposition proposed in this article can obtain more accurate prediction results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chongke Wang, Xiaofeng Lu, Gaoxiang Li, and Chen Wu "SSA-LSTM high-speed railway track irregularity prediction model based on dual-stage decomposition", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 1307810 (3 April 2024); https://doi.org/10.1117/12.3024740

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KEYWORDS

Data modeling

Singular value decomposition

Mathematical optimization

Modal decomposition

Matrices

Performance modeling

Interference (communication)

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