Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8136939 |
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| _version_ | 1832566426790002688 |
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| author | Jianhua Liu Linfan Liu Jing He Changfan Zhang Kaihui Zhao |
| author_facet | Jianhua Liu Linfan Liu Jing He Changfan Zhang Kaihui Zhao |
| author_sort | Jianhua Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a020806951254ae8a2b5dd40ead96aaa |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a020806951254ae8a2b5dd40ead96aaa2025-02-03T01:04:17ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/81369398136939Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning MachineJianhua Liu0Linfan Liu1Jing He2Changfan Zhang3Kaihui Zhao4College of Traffic Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Traffic Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaThe 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.http://dx.doi.org/10.1155/2020/8136939 |
| spellingShingle | Jianhua Liu Linfan Liu Jing He Changfan Zhang Kaihui Zhao Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine Journal of Advanced Transportation |
| title | Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine |
| title_full | Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine |
| title_fullStr | Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine |
| title_full_unstemmed | Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine |
| title_short | Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine |
| title_sort | wheel rail adhesion state identification of heavy haul locomotive based on particle swarm optimization and kernel extreme learning machine |
| url | http://dx.doi.org/10.1155/2020/8136939 |
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