Article citationsMore>>

Pan, Z., Cui, S.K., You, R.L., Xi, L.Z. and Ma, Z.G. (2022) Wear Mechanism and Reduction Measures of Sleeper Bearing Surface on Heavy Haul Railway. Railway Engineering, 62, 1-4.
https://doi.org/10.3969/j.issn.1003-1995.2022.07.01

has been cited by the following article:

• TITLE: A Prediction Method of Rail Corrugation Evolution Trend for Heavy Haul Railway Based on IPCA and ELWOA-LSSVM

  AUTHORS: Mingxia Liu, Kexin Zhang

  KEYWORDS: Rail Corrugation, PCA, Evolution Trend Prediction, WOA, LSSVM

  JOURNAL NAME: Intelligent Control and Automation, Vol.16 No.1, February 8, 2025

  ABSTRACT: Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to optimize the rail corrugation evolution trend prediction model of the least squares support vector machine (IPCA-ELWOA-LSSVM). The elite reverse learning combined with the Lévy flight strategy is introduced to improve the whale optimization algorithm. The improved WOA (ELWOA) algorithm is used to continuously optimize the kernel parameter σ and the normalization parameter γ in the LSSVM model. Finally, the improved prediction model is validated using data from a domestic heavy-duty railway experimental line database and compared with the prediction model before optimization and the other commonly used models. The experimental results show that the ELWOA-LSSVM prediction model has the highest accuracy, which proves that the proposed method has high accuracy in predicting the rail corrugation evolution trend.