A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating. (20th December 2016)

• Record Type: Journal Article
• Authors: Zhan, Liwei; Li, Chengwei
• Is Part Of: Measurement science & technology. Volume 28:Number 2(2017:Feb.)
• Publication Date: 2016-12-20
• Subjects: support vector machine -- particle swarm optimization -- friction coefficient -- aircraft tire -- coefficient of determination; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aa506d
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C, the parameter gamma γ corresponding to RBF kernel and the epsilon parameter ε in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters ( C, γ and ε ), respectively. The regression accuracy could be reflected by the coefficient of determination ( R 2 ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.