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.
References
[1]
Shang, P.P., Liu, X.B. and Ma, S. (2022) Research on Rail Service Life of Shuozhou-Huanghua Heavy Haul Railway. Railway Engineering, 62, 68-71, 85. https://doi.org/10.3969/j.issn.1003-1995.2022.08.15
[2]
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
[3]
Liu, W.T. (2021) Preliminary Analysis on Causes of Rail Corrugation on Shenmu-Shuozhou Raiway. Railway Investigation and Surveying, 47, 108-112. https://doi.org/10.19630/j.cnki.tdkc.202010260002
[4]
Jin, X.S., Li, X., Li, W. and Wen, Z.F. (2016) Review of Rail Corrugation Progress. Journal of Southwest Jiaotong University, 51, 264-273. https://doi.org/10.3969/j.issn.0258-2724.2016.02.006
[5]
Guan, Q.H., Zhang, B., Xiong, J.Y., Li, W., Wen, Z.F., Wang, H.Y. and Jin, X.S. (2021) Review on Basic Characteristics, Formation Mechanisms, and Treatment Measures of Rail Corrugation in Metro Systems. Journal of Traffic and Transportation Engineering, 21, 316-337. https://doi.org/10.19818/j.cnki.1671-1637.2021.01.015
[6]
Liu, X.Y., Wang, P. and Wan, F.G. (2000) Formation Mechanism of Rail Corrugations in Heavy-Haul Railline. Journal of the China Railway Society, 22, 98-103. https://doi.org/10.3321/j.issn:1001-8360.2000.01.021
[7]
Pavlík, A., Gerlici, J., Lack, T., Hauser, V. and Šťastniak, P. (2019) Prediction of the Rail-Wheel Contact Wear of an Innovative Bogie by Simulation Analysis. Transportation Research Procedia, 40, 855-860. https://doi.org/10.1016/j.trpro.2019.07.120
[8]
Igeland, A. (1996) Railhead Corrugation Growth Explained by Dynamic Interaction between Track and Bogie Wheelsets. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 210, 11-20. https://doi.org/10.1243/pime_proc_1996_210_322_02
[9]
Suykens, J.A.K. and Vandewalle, J. (1999) Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9, 293-300. https://doi.org/10.1023/a:1018628609742
[10]
Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D. and Vandewalle, J. (2002) Least Squares Support Vector Machines. World Scientific Publishing. https://doi.org/10.1142/9789812776655
[11]
Zuo, X.Q., Xiong, Z., Nie, L. and Ding, S.T. (2022) Study of Centrifugal Pump Fault Prediction Method Based on PSO Optimization LS-SVM. Electronic Measurement Technology, 45, 89-94. https://doi.org/10.19651/j.cnki.emt.2209552
[12]
Qu, Z., Zhao, X. and Qu, S. (2018) Least Squares Support Vector Machines Traffic Flow Forecasting Using Fruit Fly Optimization Algorithm. Electronic Measurement Technology, 41, 18-22. https://doi.org/10.19651/j.cnki.emt.1801575
[13]
Wang, Y.L. (2019) Research and Implementation of Mileage Life Prediction of Heavy-Duty Railway Freight Car Wheels. M.A. Thesis, Beijing Jiaotong University.
[14]
Liu, L., Lian, M., Lu, C., Zhang, S., Liu, R. and Xiong, N. (2022) TCSA: A Traffic Congestion Situation Assessment Scheme Based on Multi-Index Fuzzy Comprehensive Evaluation in 5G-IoV. Electronics, 11, Article 1032. https://doi.org/10.3390/electronics11071032
[15]
Yang, J., Lin, N., Zhang, K., Cui, Y., Fu, C. and Zhang, D. (2024) Deep Learning CNN-APSO-LSSVM Hybrid Fusion Model for Feature Optimization and Gas-Bearing Prediction. Petroleum Science, 21, 2329-2344. https://doi.org/10.1016/j.petsci.2024.02.012
[16]
Mirjalili, S. and Lewis, A. (2016) The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
[17]
He, Q., Huang, M.M. and Wang, X. (2019) Elite Opposition Learning-Based Dimension by Dimension Improved Dragonfly Algorithm. Journal of Nanjing Normal University (Natural Science Edition), 42, 65-72. https://doi.org/10.3969/j.issn.1001-4616.2019.03.009
[18]
Xu, J.W., Yang, P. and Yin, X.J. (2024) Gear Fault Diagnosis Based on EMD Decomposition and Levy-SSA-BP Nural Network. Journal of Mechanical Transmission, 48, 152-157. https://doi.org/10.16578/j.issn.1004.2539.2024.05.021
[19]
Liang, Q.Q., Han, H., Cui, X.Y. and Gu, B. (2016) Fault Diagnosis for Refrigeration System Based on PCA-PNN. CIESC Journal, 67, 1022-1031. https://doi.org/10.11949/j.issn.0438-1157.20151301
