Computational Technology Resources - CCC

O. Vo Van¹ and V. Laurent²

¹Direction Technologies, Innovation et Projets Groupe, SNCF, France
²Centre Borelli, ENS Paris-Saclay, Université Paris-Saclay, France

doi:10.4203/ccc.7.8.1

O. Vo Van, V. Laurent, "Maintenance Applications of a Machine Learning Model of Rail Defects", 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 8.1, 2024, doi:10.4203/ccc.7.8.1

Keywords: machine learning, rail, random forest, prescriptive maintenance, predictive maintenance, rolling contact fatigue.

Abstract

Rolling contact fatigue is the first cause of rail degradation. The phenomenon is deemed random yet depending on many exogenous parameters. Numerical physical modeling has shown its limits and data modeling had to be introduced. In this paper, one proposes to show diverse applications of a hybrid model using data and physics in an industrial context. The data architecture linking physical model, random forest classification model and survival forest model is quickly exposed. Then some indicators are proposed to eval the model performance. Three applications are then developed based on this mixed-model, phenomenon understanding - closely linked with model validation by expert knowledge -, predictive maintenance scheduling by rail grinding, and prescriptive maintenance illustrated by fixing the time delay before the first visit by ultrasound engines.

download the full-text of this paper (PDF, 1559 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
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 8.1 Maintenance Applications of a Machine Learning Model of Rail Defects O. Vo Van¹ and V. Laurent² ¹Direction Technologies, Innovation et Projets Groupe, SNCF, France ²Centre Borelli, ENS Paris-Saclay, Université Paris-Saclay, France doi:10.4203/ccc.7.8.1 Full Bibliographic Reference for this paper O. Vo Van, V. Laurent, "Maintenance Applications of a Machine Learning Model of Rail Defects", 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 8.1, 2024, doi:10.4203/ccc.7.8.1 Keywords: machine learning, rail, random forest, prescriptive maintenance, predictive maintenance, rolling contact fatigue. Abstract Rolling contact fatigue is the first cause of rail degradation. The phenomenon is deemed random yet depending on many exogenous parameters. Numerical physical modeling has shown its limits and data modeling had to be introduced. In this paper, one proposes to show diverse applications of a hybrid model using data and physics in an industrial context. The data architecture linking physical model, random forest classification model and survival forest model is quickly exposed. Then some indicators are proposed to eval the model performance. Three applications are then developed based on this mixed-model, phenomenon understanding - closely linked with model validation by expert knowledge -, predictive maintenance scheduling by rail grinding, and prescriptive maintenance illustrated by fixing the time delay before the first visit by ultrasound engines. download the full-text of this paper (PDF, 1559 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
Back to top	©Civil-Comp Limited 2023 - terms & conditions