WheelRail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine
Joint Authors
Zhang, Chang-fan
He, Jing
Zhao, Kaihui
Liu, Jianhua
Liu, Linfan
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-10
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Jianhua& Liu, Linfan& He, Jing& Zhang, Chang-fan& Zhao, Kaihui. 2020. WheelRail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1176085
Modern Language Association (MLA)
Liu, Jianhua…[et al.]. WheelRail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine. Journal of Advanced Transportation No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1176085
American Medical Association (AMA)
Liu, Jianhua& Liu, Linfan& He, Jing& Zhang, Chang-fan& Zhao, Kaihui. WheelRail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1176085
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1176085