@inproceedings{turgis_health_2022,
	address = {Birmingham, United Kingdom},
	title = {Health state characterization using clustering algorithms for railway maintenance},
	abstract = {To perform maintenance of a large rolling fleet, with operational constraints due to mass transit, a mixed maintenance solution combining real-time data analysis and condition-based maintenance has been integrated into the SNCF maintenance process in 2017. Based on a prognostic expert system, this solution relies on constant signaling thresholds defined using technical knowledge and physical models to assess the health state of a system. As the health of a system differs from one train to another and independently evolves in time, constant signaling thresholds do not always take into account maintenance load, maintenance infrastructure availabilities and effect of aging during the lifetime of the system. To overtake these limitations and enhance the current maintenance solution, an upgrade of the existing expert system has been developed and funded by SNCF Materiel, using dynamic signaling thresholds, based on the study of health indicators distribution across the whole fleet. This article describes the concept of this new hybrid system, mixing expert system and machine learning tools. It shows how dynamic thresholds can be computed and how clustering algorithms can be used to identify and characterize potential failure modes},
	author = {Turgis, Fabien and Audier, Pierre and Nemoz, Valentin and Marion, Rémy},
	year = {2022},
}

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