Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Jianhua Liu,Linfan Liu,Kaihui Zhao,Jing He,Changfan Zhang

doi:10.1155/2020/8136939

Abstract

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

Highlights

Academic Editor: Paola Pellegrini e traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states
A wheel/rail adhesion state identi cation method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed
Based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the e ectiveness and feasibility of the proposed method

Summary

Introduction

Academic Editor: Paola Pellegrini e traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. A wheel/rail adhesion state identi cation method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identi cation model is constructed using KELM, and the regularization coe cient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. The main challenge of the model-based methods is to create a model that exactly characterizes the physical system in all conditions, and the identi cation of its precision is impacted by uncertain nonlinear parameters, unknown noises, and others factors. The precision of identi cation is a ected by uncertain nonlinear parameters, unknown noises and other factors

Methods

Results

Conclusion