Computational Technology Resources - CCC

M. Saiem¹, B. Nelain², F. Hnaien³ and H. Snoussi³

¹, University of Technology of Troyes, Troyes, France, France
², Vossloh Switch Systems, France
³, LIST3N, University of Technology of Troyes, France

doi:10.4203/ccc.15.6.13

M. Saiem, B. Nelain, F. Hnaien, H. Snoussi, "A Data-Driven Chance-Constrained Receding-Horizon Framework for Railway Turnout Maintenance Optimization", 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 6.13, 2026, doi:10.4203/ccc.15.6.13

Keywords: predictive maintenance, railway turnouts, stochastic degradation modeling, chance-constrained optimization, receding-horizon optimization, lifecycle cost, Monte Carlo simulation.

Abstract

This study presents a data-driven stochastic framework for degradation modeling and maintenance optimization of railway components parts under uncertainty. The proposed framework integrates data-driven degradation and repair-effectiveness models with an MDP-based regime-transition model, enabling the representation of realistic asset life-cycle behavior under changing conditions. Building on these stochastic degradation scenarios, a chance-constrained receding-horizon optimization framework is developed to support maintenance and renewal decision-making. Maintenance activities are evaluated using risk-adjusted cost, and reliability indicators, while chance constraints ensure compliance with predefined risk thresholds. The approach was validated using real datasets. The resulting framework enables adaptive maintenance planning that balances economic performance and system reliability under uncertain degradation conditions.

download the full-text of this paper (PDF, 14 pages, 958 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: 15 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 6.13 A Data-Driven Chance-Constrained Receding-Horizon Framework for Railway Turnout Maintenance Optimization M. Saiem¹, B. Nelain², F. Hnaien³ and H. Snoussi³ ¹, University of Technology of Troyes, Troyes, France, France ², Vossloh Switch Systems, France ³, LIST3N, University of Technology of Troyes, France doi:10.4203/ccc.15.6.13 Full Bibliographic Reference for this paper M. Saiem, B. Nelain, F. Hnaien, H. Snoussi, "A Data-Driven Chance-Constrained Receding-Horizon Framework for Railway Turnout Maintenance Optimization", 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 6.13, 2026, doi:10.4203/ccc.15.6.13 Keywords: predictive maintenance, railway turnouts, stochastic degradation modeling, chance-constrained optimization, receding-horizon optimization, lifecycle cost, Monte Carlo simulation. Abstract This study presents a data-driven stochastic framework for degradation modeling and maintenance optimization of railway components parts under uncertainty. The proposed framework integrates data-driven degradation and repair-effectiveness models with an MDP-based regime-transition model, enabling the representation of realistic asset life-cycle behavior under changing conditions. Building on these stochastic degradation scenarios, a chance-constrained receding-horizon optimization framework is developed to support maintenance and renewal decision-making. Maintenance activities are evaluated using risk-adjusted cost, and reliability indicators, while chance constraints ensure compliance with predefined risk thresholds. The approach was validated using real datasets. The resulting framework enables adaptive maintenance planning that balances economic performance and system reliability under uncertain degradation conditions. download the full-text of this paper (PDF, 14 pages, 958 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