Abstract
The wheel wear is a dynamic phenomenon that varies with many mechanical and geometrical factors. Accurately estimating wheel wear is a vital issue in wheel maintance. This paper presents a nature-inspired metaheuristic regression method for precisely predicting wheel status that combines least squares support vector machine (LS-SVM) with a novel PSO-GA-LM algorithm. The PSO-GA-LM algorithm integrates Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Logistic Map (LM). The method is used to optimize the hyper-parameters of the LS-SVM model. The proposed model was constructed with datasets of the tread wear derived from Taiyuan North Locomotive Depot. Analytical results show that the novel optimized prediction model is superior to others in predicting tread wear with lower RMSE (0.037MPa), MAE (0.027MPa) and MAPE (0.0008 %).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fries, R.H., Dvila, C.G.: Analytical methods for wheel and rail wear prediction. Veh. Syst. Dyn. 15(15), 112–125 (1986)
Enblom, R., Dirks, B.: Prediction model for wheel profile wear and rolling contact fatigue. Wear 271(1), 210–217 (2011)
Ding, J.J., Sun, S.L., Fu, L.I., Huang, Y.H.: Simulation of wheelwear for heavy haul freight car. J. Traffic Transp. Eng. 11(4), 56–60 (2011)
Gu, B., Sheng, V.S.: A robust regularization path algorithm for -support vector classification. In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1–8 (2016)
Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2014)
Gu, B., Sun, X., Sheng, V.S.: Structural minimax probability machine. In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11 (2016)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Chun, Y.: LSSVM based constitutive model of geomaterial used in element analysis. J. Hebei Univ. Eng. (Natural Science Edition) 30(3), 26–29 (2013)
Chou, J.S., Ngo, N.T., Pham, A.D., Ngo, N.T.: Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. J. Comput. Civil Eng. 39(8), 945–949 (2015)
Czarnecki, W.M., Podlewska, S., Bojarski, A.J.: Robust optimization of SVM hyperparameters in the classification of bioactive compounds. J. Cheminformatics 7(1), 1–15 (2015)
Lou, I., Xie, Z., Ung, W.K., Kai, M.M.: Integrating support vector regression with particle swarm optimization for numerical modeling for algal blooms of freshwater. Appl. Math. Model. 39(19), 5907–5916 (2015)
Geng, L.Y., Zhang, T.W., Zhao, P.: Forecast of railway freight volumes based on LS-SVM with grey correlation analysis. J. China Railway Soc. 34(3), 1–6 (2012)
Guo, X.C., Yang, J.H., Wu, C.G., Wang, C.Y., Liang, Y.C.: A novel LS-SVMS hyper-parameter selection based on particle swarm optimization. Neurocomputing 71(16–18), 3211–3215 (2008)
Gupta, A.R., Ingle, V.R., Gaikwad, M.A.: LS-SVM parameter optimization using genetic algorithm to improve fault classification of power transformer. Int. J. Eng. Res. Appl. 2(4), 1806–1809 (2012)
Ma, X.J., Ren, S.H., Zuo, H.F., Wen, Z.H.: Prediction method of aero-enginelife on wing based on LS-SVM algorithm and performance reliability. J. Traffic Transp. Eng. 15(3), 92–100 (2015)
Zhong, L., Cheng, L., Gong, J.: Prediction of wheel tread wear volume based on least squares support vector machine optimized by coupled simulated annealing. Appl. Res. Comput. 32(2), 397–402 (2015)
Wang, Y., Zuo, H., Cai, J., Rong, X.: Forecasting model of engine life on wing based on LS-SVM and Bayesian inference. J. Nanjing Univ. Sci. Technol. 37(6), 955–960 (2013)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999 (1999)
Acknowledgments
This work was supported by Funding of National Natural Science Foundation of China (Grant No. 61571226), Jiangsu Program for the transformation of scientific and technological achievements (BA2015051).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Hua, S., Yuan, J., Ding, W. (2016). LS-SVM-Based Prediction Model of Tread Wear Optimized by PSO-GA-LM. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-48674-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48673-4
Online ISBN: 978-3-319-48674-1
eBook Packages: Computer ScienceComputer Science (R0)