Computational Technology Resources - CCC

M. Słowiński¹, T. Staśkiewicz¹, K. Grochalski² and B. Jakubek³

¹Institute of Transport, Poznan University of Technology, Poland
²Institute of Mechanical Engineering, Poznan University of Technology, Poland
³Institute of Applied Mechanics, Poznan University of Technology, Poland

doi:10.4203/ccc.7.9.7

M. Słowiński, T. Staśkiewicz, K. Grochalski, B. Jakubek, "Thermal Vision Analysis of Wheel-Rail Interaction: Application of Convolutional Neural Networks", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 9.7, 2024, doi:10.4203/ccc.7.9.7

Keywords: wheel-rail interface, thermal imaging, convolutional neural networks, image classification, frictional heating, creepages.

Abstract

This paper focused on the application of a Convolutional Neural Network in the analysis of the thermal images of the wheel-rail interface. The first part is centered on the primary motivations behind conducting thermal imaging measurements in the wheel-rail contact area and the analysis of the thermograms using Convolutional Neural Networks. It was emphasized that automatic classification of the wheel-rail contact types can provide valuable insights, especially considering the spatial data and statistical metrics, which can be useful in the case of wear intensity analysis. Subsequently, the methodology of thermal imaging measurement and the design of a classifier utilizing Convolutional Neural Network technology were presented. In the context of result analysis, the very promising capabilities of detecting various contact types using Convolutional Neural Network s were highlighted. The paper concluded by summarizing the key benefits arising from the proposed technology and its potential impact on wheel-rail interaction studies.

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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: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 9.7 Thermal Vision Analysis of Wheel-Rail Interaction: Application of Convolutional Neural Networks M. Słowiński¹, T. Staśkiewicz¹, K. Grochalski² and B. Jakubek³ ¹Institute of Transport, Poznan University of Technology, Poland ²Institute of Mechanical Engineering, Poznan University of Technology, Poland ³Institute of Applied Mechanics, Poznan University of Technology, Poland doi:10.4203/ccc.7.9.7 Full Bibliographic Reference for this paper M. Słowiński, T. Staśkiewicz, K. Grochalski, B. Jakubek, "Thermal Vision Analysis of Wheel-Rail Interaction: Application of Convolutional Neural Networks", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 9.7, 2024, doi:10.4203/ccc.7.9.7 Keywords: wheel-rail interface, thermal imaging, convolutional neural networks, image classification, frictional heating, creepages. Abstract This paper focused on the application of a Convolutional Neural Network in the analysis of the thermal images of the wheel-rail interface. The first part is centered on the primary motivations behind conducting thermal imaging measurements in the wheel-rail contact area and the analysis of the thermograms using Convolutional Neural Networks. It was emphasized that automatic classification of the wheel-rail contact types can provide valuable insights, especially considering the spatial data and statistical metrics, which can be useful in the case of wear intensity analysis. Subsequently, the methodology of thermal imaging measurement and the design of a classifier utilizing Convolutional Neural Network technology were presented. In the context of result analysis, the very promising capabilities of detecting various contact types using Convolutional Neural Network s were highlighted. The paper concluded by summarizing the key benefits arising from the proposed technology and its potential impact on wheel-rail interaction studies. download the full-text of this paper (PDF, 5 pages, 513 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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