Computational Technology Resources - CCC

Z. Hu^1,2, L. Chen¹, Y. Song^1,3, P. Antunes², Z. Liu¹ and J. Pombo²

¹School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
²Institute of Railway Research, University of Huddersfield, United Kingdom
³SWJTU-Leeds Joint School, Southwest Jiaotong University, Chengdu, China

doi:10.4203/ccc.15.10.6

Z. Hu, L. Chen, Y. Song, P. Antunes, Z. Liu, J. Pombo, "Integrated Framework for Feature Selection and Dynamic Performance Prediction of Overhead Conductor Rail", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance ", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 15, Paper 10.6, 2026, doi:10.4203/ccc.15.10.6

Keywords: electrified railways, overhead conductor rail, dynamic performance, feature selection, contact force, deep learning.

Abstract

Overhead conductor rail (OCR) is essential for traction power supply. However, as operating speeds increase, the dynamic performance of the pantograph-overhead conductor rail system (POCR) deteriorates significantly. To achieve low-cost and efficient monitoring, a sparse autoencoder (SAE) is introduced to evaluate node importance based on the encoding weight matrix. By incorporating a minimum spacing constraint, the master nodes contributing most significantly to the dynamic performance of the POCR are identified and found to be predominantly distributed at the supports. Subsequently, a bidirectional long short-term memory (BiLSTM) model is developed to predict the contact force and contact point movement using the vertical displacements of these master nodes as inputs. Results indicate that the root mean square error for contact force and contact point movement are 0.096 and 0.087, respectively, demonstrating the accuracy of the proposed method in capturing non-linear dynamic responses of the POCR.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 15 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 10.6 Integrated Framework for Feature Selection and Dynamic Performance Prediction of Overhead Conductor Rail Z. Hu^1,2, L. Chen¹, Y. Song^1,3, P. Antunes², Z. Liu¹ and J. Pombo² ¹School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China ²Institute of Railway Research, University of Huddersfield, United Kingdom ³SWJTU-Leeds Joint School, Southwest Jiaotong University, Chengdu, China doi:10.4203/ccc.15.10.6 Full Bibliographic Reference for this paper Z. Hu, L. Chen, Y. Song, P. Antunes, Z. Liu, J. Pombo, "Integrated Framework for Feature Selection and Dynamic Performance Prediction of Overhead Conductor Rail", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance ", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 15, Paper 10.6, 2026, doi:10.4203/ccc.15.10.6 Keywords: electrified railways, overhead conductor rail, dynamic performance, feature selection, contact force, deep learning. Abstract Overhead conductor rail (OCR) is essential for traction power supply. However, as operating speeds increase, the dynamic performance of the pantograph-overhead conductor rail system (POCR) deteriorates significantly. To achieve low-cost and efficient monitoring, a sparse autoencoder (SAE) is introduced to evaluate node importance based on the encoding weight matrix. By incorporating a minimum spacing constraint, the master nodes contributing most significantly to the dynamic performance of the POCR are identified and found to be predominantly distributed at the supports. Subsequently, a bidirectional long short-term memory (BiLSTM) model is developed to predict the contact force and contact point movement using the vertical displacements of these master nodes as inputs. Results indicate that the root mean square error for contact force and contact point movement are 0.096 and 0.087, respectively, demonstrating the accuracy of the proposed method in capturing non-linear dynamic responses of the POCR. download the full-text of this paper (PDF, 8 pages, 818 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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