LS-SVM-Based Prediction Model of Tread Wear Optimized by PSO-GA-LM

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 %).