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INNOvative monitoring and predictive maintenance solutions on lightweight WAGon

Periodic Reporting for period 2 - INNOWAG (INNOvative monitoring and predictive maintenance solutions on lightweight WAGon)

Reporting period: 2018-05-01 to 2019-06-30

The rail freight challenge is to increase its competitiveness and attractiveness through a higher productivity and efficiency, as well as by adding new features that would respond to customers and end-users demands. INNOWAG tackled internal drivers of change that are specific to the rail freight market (e.g. time and distance of services, demand for new logistic services, etc.) with the overall goal to increase the rail market share in accessible segments, and thus support the shift of freight transport to rail.
Project's objectives and approach
• Increase rail freight efficiency and capacity by optimising and lightweighting the wagon design for increased ratio of payload to wagon tare
• Increase freight logistic capabilities by:
1. offering real time data on freight location and condition through a smart self-powered sensor system and communication technologies
2. optimised wagon modular design capable to transport various types of goods
3. improved availability to freight customers, enabled by a safer and more reliable and interoperable freight service
• Increase Reliability, Availability, Maintainability and Safety (RAMS), and reduce Life Cycle Costs (LCC) of wagons and freight services by implementing modern and innovative predictive maintenance analytics, models, and procedures.
Initial work analysed the market drivers and trends in the areas of the project and benchmarked existing and emerging solutions; performance indicators and technical specifications were defined. Further research focused on the development of innovations in three macro-areas, from concept to laboratory and real environment testing. Specific challenges in these essential areas were tackled separately by the INNOWAG work streams.
Work Stream 1: CARGO CONDITION MONITORING
Scope: Development of an autonomous self-powered sensor system for cargo tracing and condition monitoring of key parameters for critical types of freight, such as perishable goods, high value sensitive goods and dangerous goods, by integrating novel technologies and solutions.
Activities and results
• Definition of potential sensor system architectures and design of solutions dedicated to the defined cases
• Analysis of energy consumption and design of energy harvesting solutions (vibration energy harvester, radio frequency based energy harvesting and transfer, and solar cells)
• Analysis of communication solutions and design of different communication hub concepts, integrating GPS for tracking, GPRS for remote data transmission and either Bluetooth or RFID for communication with sensors
• Implementation of energy harvesting and Cargo Condition Monitoring System prototypes
• Preliminary testing of prototypes in laboratory (TRL 3-4)
• Testing and demonstration of prototypes in controlled environment (TRL 5) and/or in rail freight operation (TRL 6-7).
Work Stream 2: WAGON DESIGN
Scope: Development of a novel concept of modular and lightweight wagon.
Activities and results
• Analysis and selection of materials
• Design of five different lightweight concepts for three different technologies (using advanced materials along with optimised design):
- Lightweight Y25 bogie (17% mass reduction)
- Lightweight 60' container wagon (22% mass reduction)
- Lightweight cereals hopper wagon in 3 versions (mass reduction between 21-27% and 13.3% capacity increase for 1 design)
• Modelling and analysis of lightweight structural solutions to support refinement of design concepts and validate the solutions
• Implementation and testing of samples and prototypes, including:
- Full scale prototypes (bogie frame and composite panel for hopper side walls)
- Different relevant samples (hybrid joints steel - composites, impact testing and abrasion testing of composites).
Work Stream 3: PREDICTIVE MAINTENANCE
Scope: Development of an integrated predictive maintenance approach to enable efficient use of both remote condition monitoring and historical data, and further support the implementation of predictive models and tools in rolling stock maintenance programmes.
Activities and results
• RAMS and LCC analyses based on sets of real data from maintenance activities and components failure data
• Definition of Prioritisation List of critical Y25 bogie components in terms of their relevance for predictive maintenance
• Development of data processing methods for the structural health monitoring of bogie components
• Study on the detection of short-size wheel flat and its propagation to a large size by means of analytical models, simulation and field data
• Definition of a guided procedure for a predictive maintenance policy for Y25 bogies based on available historical data, condition monitoring data and maintenance costs
• Software tool “Wizard tool for maintenance policy optimisation” synthesising the procedure for predictive maintenance; the tool was tested through two case studies.
An investigation of the opportunities for integration and complementarity was also carried out and showed that there are significant opportunities across the INNOWAG concepts to deliver an integrated, cost effective and low risk lightweight smart freight wagon solution.
Cargo Condition Monitoring
Prototype systems were implemented and demonstrated, showing that the key technical challenges were successfully addressed and that the developed technical concepts are technically viable. Commercialisation and adoption of the developed solutions would contribute towards the attractiveness of rail freight by enabling the provision of a service and capability that is not currently available in a commercially viable form. The techniques and technologies developed could also be applied to other applications, such as vehicle condition monitoring.
Wagon Design
The methodology for the assessment and selection of novel materials for lightweight wagon design is an important step forward in the practical implementation of such materials by industry.
The lightweighting process based on the use of advanced materials along with design optimisation techniques could be directly implemented by industry, and the lightweight INNOWAG concepts are already a good starting point (TRL 5-6) for further certification and market uptake of novel wagons. In addition, the validation methodology based on both numerical modelling and simulation, and laboratory testing is a sound guideline for the necessary steps in the development and implementation of freight rolling stock.
Predictive Maintenance
The development of a predictive maintenance approach for wagons is a significant innovation in rail vehicles maintenance. There are currently no predictive maintenance strategies that integrate the analysis of historical and condition monitoring data in use for wagons. The development and application of an assessment tool to evaluate the effect of adopting predictive maintenance also represents a significant advance, as this has not been done before for rail vehicles and will support the adoption of predictive maintenance by enabling business cases to be built where it is quantitatively shown to be beneficial. A comprehensive evaluation and quantification of LCC is also a useful tool to prioritise the future deployment of condition monitoring and predictive maintenance practice for wagons.
INNOWAG - project concept
Lightweight wagon design concepts developed by INNOWAG
Generic architecture of INNOWAG Cargo Condition Monitoring System
INNOWAG - challenge and approach