Periodic Reporting for period 2 - FP3 - IAM4RAIL (Europe’s Rail Flagship Project 3 - Holistic and Integrated Asset Management for Europe’s RAIL System)
Período documentado: 2024-01-01 hasta 2024-12-31
The FP3–IAM4RAIL flagship project aims to transform the rail sector by providing innovative technical solutions, leveraging cutting-edge technologies to minimise asset lifecycle costs, enhance safety and improve reliability, availability and capacity of railroad systems. Focusing on infrastructure and rolling stock, it seeks to establish a common European asset management framework comprising green, digital and safe solutions. The project emphasises cost-effective asset management, automated construction and environmentally friendly production. Its goal is integrating asset status information with TMS (Train Management Systems) for optimised decision-making and lifecycle management. Through research, development and demonstrations across Europe, the project targets mostly TRL 6/7 for integrated solutions, with a focus on future certification and validation.
The three main objectives of FP3–IAM4RAIL are:
1. Cost-Effective Asset Management with Digital Technologies: develop a European asset management framework using digital technologies and data analytics for infrastructure and rolling stock interventions. This includes integrating supply chain information, TMS connectivity, advanced diagnostics and efficient maintenance planning through Condition-Based Maintenance (CBM) methodologies and algorithms for freight rail applications.
2. Advanced and Holistic Asset Decisions: develop methodologies and technologies for informed lifecycle decisions, including probabilistic models for lifecycle costs, cooperative diagnosis and AI-based decision support. These solutions enhance decision-making, predict anomalies and optimise maintenance strategies.
3. Environmentally Friendly Asset Production: establish environmentally friendly production processes for resilient assets by leveraging innovative design principles, fabrication techniques and materials, ensuring sustainability and durability. This will lead to significant improvements in the railway sector.
Demonstration activities are grouped into five clusters:
· Cluster B - Wayside Monitoring and TMS Link: securely collecting, storing and analysing wayside asset data to enhance predictive maintenance and intelligent asset management and establishing the link between asset information and TMS.
· Cluster C - Rolling Stock Asset Management: on-board and wayside monitoring technologies for rolling stock or mounted on it.
· Cluster D - Infrastructure Asset Management: addressing maintenance planning, track systems management, innovative infrastructure applications and civil asset management, based on extracting asset condition from infrastructure monitoring.
· Cluster E - Railway Digital Twins: implementing Digital Twins to optimise asset-related processes.
· Cluster F - Environment, User, and Worker Friendly Railway Assets: showcasing environmentally, user and worker-friendly rail assets, including unmanned, automatic and/or robotic inspections and interventions.
The three main objectives of FP3–IAM4RAIL are:
1. Cost-Effective Asset Management with Digital Technologies: develop a European asset management framework using digital technologies and data analytics for infrastructure and rolling stock interventions. This includes integrating supply chain information, TMS connectivity, advanced diagnostics and efficient maintenance planning through Condition-Based Maintenance (CBM) methodologies and algorithms for freight rail applications.
2. Advanced and Holistic Asset Decisions: develop methodologies and technologies for informed lifecycle decisions, including probabilistic models for lifecycle costs, cooperative diagnosis and AI-based decision support. These solutions enhance decision-making, predict anomalies and optimise maintenance strategies.
3. Environmentally Friendly Asset Production: establish environmentally friendly production processes for resilient assets by leveraging innovative design principles, fabrication techniques and materials, ensuring sustainability and durability. This will lead to significant improvements in the railway sector.
Demonstration activities are grouped into five clusters:
· Cluster B - Wayside Monitoring and TMS Link: securely collecting, storing and analysing wayside asset data to enhance predictive maintenance and intelligent asset management and establishing the link between asset information and TMS.
· Cluster C - Rolling Stock Asset Management: on-board and wayside monitoring technologies for rolling stock or mounted on it.
· Cluster D - Infrastructure Asset Management: addressing maintenance planning, track systems management, innovative infrastructure applications and civil asset management, based on extracting asset condition from infrastructure monitoring.
· Cluster E - Railway Digital Twins: implementing Digital Twins to optimise asset-related processes.
· Cluster F - Environment, User, and Worker Friendly Railway Assets: showcasing environmentally, user and worker-friendly rail assets, including unmanned, automatic and/or robotic inspections and interventions.
IAM4RAIL made significant progress in defining technical specifications, requirements and demonstration activities for use cases, with efforts concentrated on defining KPIs, designing technologies and initiating data collection.
Specific activities per cluster:
· Cluster B - Wayside Monitoring and TMS Link: developed an Intelligent Asset Management System (IAMS) for wayside assets, including data collection, analysis and TMS integration. Early data collection supported Digital Twins development, with sensor installations in Italy and Spain. Dashboards for monitoring and anomaly detection were created, and alignment activities with FA1 were initiated. Best practices for TMS and Decision Support Systems (DSS) were defined.
· Cluster C - Rolling Stock Asset Management: developed monitoring and inspection systems, initiated data acquisition technologies and designed anomaly detection algorithms for Bogie & Traction subsystems. Innovative Bogie sensing technologies were tested, and a CBM safety certification method was developed. Challenges in obtaining historical maintenance data were addressed, and European Railway Checkpoints' common use cases and standards were defined.
· Cluster D - Infrastructure Asset Management: conducted research, defined KPIs and initiated data collection across multiple countries. Developed decision-support tools for long-term infrastructure maintenance, monitored vehicle-track interactions with onboard sensors and machine learning and created intelligent railway sleepers using graphene-enhanced concrete.
· Cluster E - Railway Digital Twins: focused on implementing Building Information Modelling (BIM) replicas, linking survey and diagnostic data to Digital Twins (DT) and processing DT asset data. This included a station cleanliness detection system and Blockchain and Virtual Certification Framework specifications.
· Cluster F - Environment, User, and Worker Friendly Railway Assets: defined use cases, specifications and requirements, initiating technical activities. Developed sustainable eco-design for rail assets, established principles of design and specific coefficients for the design of structures based on dynamic effects of trainsets passing, tested elastomers and flame-retardant polymers for Additive Manufacturing (AM) and advanced robotic platforms for railway interventions. Exoskeletons and Augmented Reality (AR) technologies were also developed for railway maintenance, alongside guidelines for ecosystem development and integration.
Specific activities per cluster:
· Cluster B - Wayside Monitoring and TMS Link: developed an Intelligent Asset Management System (IAMS) for wayside assets, including data collection, analysis and TMS integration. Early data collection supported Digital Twins development, with sensor installations in Italy and Spain. Dashboards for monitoring and anomaly detection were created, and alignment activities with FA1 were initiated. Best practices for TMS and Decision Support Systems (DSS) were defined.
· Cluster C - Rolling Stock Asset Management: developed monitoring and inspection systems, initiated data acquisition technologies and designed anomaly detection algorithms for Bogie & Traction subsystems. Innovative Bogie sensing technologies were tested, and a CBM safety certification method was developed. Challenges in obtaining historical maintenance data were addressed, and European Railway Checkpoints' common use cases and standards were defined.
· Cluster D - Infrastructure Asset Management: conducted research, defined KPIs and initiated data collection across multiple countries. Developed decision-support tools for long-term infrastructure maintenance, monitored vehicle-track interactions with onboard sensors and machine learning and created intelligent railway sleepers using graphene-enhanced concrete.
· Cluster E - Railway Digital Twins: focused on implementing Building Information Modelling (BIM) replicas, linking survey and diagnostic data to Digital Twins (DT) and processing DT asset data. This included a station cleanliness detection system and Blockchain and Virtual Certification Framework specifications.
· Cluster F - Environment, User, and Worker Friendly Railway Assets: defined use cases, specifications and requirements, initiating technical activities. Developed sustainable eco-design for rail assets, established principles of design and specific coefficients for the design of structures based on dynamic effects of trainsets passing, tested elastomers and flame-retardant polymers for Additive Manufacturing (AM) and advanced robotic platforms for railway interventions. Exoskeletons and Augmented Reality (AR) technologies were also developed for railway maintenance, alongside guidelines for ecosystem development and integration.
IAM4RAIL has defined technical specifications and requirements for innovative rail asset management solutions. By focusing on digital technologies and data analytics, the project aims to revolutionise asset management, minimising lifecycle costs and enhancing safety. Advanced decision-making methodologies, including probabilistic models and AI-based decision support, highlight the project’s commitment to progress in railway asset management. Environmentally friendly production processes mark a significant departure from traditional manufacturing, leveraging new design principles, fabrication techniques and materials to reduce environmental impact while ensuring quality and durability. The project's integrated solutions aim to optimise asset lifecycle management, enhancing reliability, availability and capacity. By achieving TRL 6/7, the project sets the stage for widespread adoption and commercialisation, making a significant impact on the European railway industry.