IN2SMART is a project of the Shift2Rail Joint Undertaking, according to Shift2Rail Multi-Annual Action Plan (MAAP), and is made of the following three interlinked Technology Demonstrators (TDs):
- TD3.7: Railway Information Measuring and Monitoring System (RIMMS).
- TD3.6: Dynamic Railway Information Management System (DRIMS).
- TD3.8: Intelligent Asset Management Strategies (IAMS).
IN2SMART delivers the strategic research and early stage development essential to fulfil the TDs objectives in their entirety within the framework of Shift2Rail. These TDs deploy an overall concept for Intelligent Asset Management based on the following three main interlinked layers:
- Measuring and Monitoring systems to collect data from the field related to the railway assets status: IN2SMART develops unmanned systems for “remote” monitoring; track geometry, switches & crossings and signalling monitoring systems; innovative measurement of train parameters and wheel defects combined with rolling stock identifications systems.
- Data management, data mining and data analytics procedures to process data from the field and from other sources: IN2SMART develops standard open interfaces to access heterogeneous maintenance-related data; analytic tools to automatic detect anomalies, discover and describe maintenance workflow processes and predict railway assets decay towards prescriptive maintenance.
- Degradation models and decision making tools to support maintenance strategies and execution: IN2SMART lays the foundation of a generic framework for asset management and decision support process. This framework specifies the scope, objectives, workflow and the outcomes of the decision-making process for maintenance interventions planning, and is the enabler for the development of future decision support tools and systems. IN2SMART also develops an optimised tamping tool and a robot platform for maintenance works.
IN2SMART complements the work of the IN2RAIL lighthouse project to reach a homogeneous TRL4/5 demonstrator.
The IN2SMART consortium is composed by 7 founding members and 12 associated members where CEMOSA take part as construction and maintenance specialist. The majority of the members of the consortium have worked collaboratively for many years on these topics in different EU-funded projects.
CEMOSA contributes in the development of advanced algorithms for defect detection and degradation in infrastructure components, with special focus on the prediction of defect initiation, which is tightly related to failure rates used in RAMS&LCC analysis.
Moreover, CEMOSA develops the generic framework for decision support in maintenance and interventions planning.