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 %).
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-48674-1_45
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