This paper presents the objectives and the main expected results from IN2SMART Project, funded by the SHIFT2RAIL Joint Undertaking and the European Commission, within the SHIFT2RAIL Research Programme. This project contributes to the development of an intelligent and automated platform for Asset Management decision-making, focused on the planning of predictive, condition and risk-based Asset Management activities. Based on a framework for Asset Management aligned with international standards, the platform receives inputs from tools and models for predictive analytics that are able to extract information on current and future asset condition, using heterogeneous data from the field. In particular, nowcasting and forecasting methodologies, diagnostics and anomaly detection techniques and indicators derived from Risk, RAMS and LCC analysis are used to support decision-making. Finally, real-world business cases are presented to show the expected applicability of the proposed automated platform and the usefulness of the relevant methodology.

Towards an intelligent and automated platform for railway Asset Management

Alice Consilvio;Federico Papa
2018-01-01

Abstract

This paper presents the objectives and the main expected results from IN2SMART Project, funded by the SHIFT2RAIL Joint Undertaking and the European Commission, within the SHIFT2RAIL Research Programme. This project contributes to the development of an intelligent and automated platform for Asset Management decision-making, focused on the planning of predictive, condition and risk-based Asset Management activities. Based on a framework for Asset Management aligned with international standards, the platform receives inputs from tools and models for predictive analytics that are able to extract information on current and future asset condition, using heterogeneous data from the field. In particular, nowcasting and forecasting methodologies, diagnostics and anomaly detection techniques and indicators derived from Risk, RAMS and LCC analysis are used to support decision-making. Finally, real-world business cases are presented to show the expected applicability of the proposed automated platform and the usefulness of the relevant methodology.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/971919
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