Community Research and Development Information Service - CORDIS

H2020

INNOWAG Report Summary

Project ID: 730863
Funded under: H2020-EU.3.4.8.5.

Periodic Reporting for period 1 - INNOWAG (INNOvative monitoring and predictive maintenance solutions on lightweight WAGon)

Reporting period: 2016-11-01 to 2018-04-30

Summary of the context and overall objectives of the project

The Challenge
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.
Innovation in rail freight is driven by both external drivers of change (such as demographics, globalisation, technology, sustainability, regulation, etc.) and internal drivers of change that are specific to the rail freight market (e.g., time and distance of services, demand of new logistic services, etc.). Such requirements have to be fulfilled through development of hardware and software technologies to be implemented in new equipment design and management of information.
INNOWAG tackles the internal drivers of change, with the overall goal to increase the rail market share in accessible segments, and thus support the shift of freight transport to rail. INNOWAG develops innovations in three macro-areas, from concept to laboratory and real environment testing, in view of further integration and implementation.

Project's objectives and approach
• Increase freight rail capacity by optimising and lightweighting the wagon design for increasing the ratio payload/wagon tare;
• Increase freight logistic capabilities by:
i. offering real time data on freight location and condition through a smart self-powered sensor system and communication technologies;
ii. optimised wagon modular design capable to transport various types of goods; and
iii. improved availability to freight customers, enabled by a safer and more reliable and interoperable freight service;
• Increase RAMS and reduce LCC by implementing modern and innovative predictive maintenance analytics, models, and procedures.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The starting work analysed the market drivers and trends in all three areas of the project. A benchmark of existing and emerging solutions was carried out, and performance indicators and technical specifications were defined (including specific INNOWAG Performance Indicators alongside the S2R KPIs).

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.

Results achieved in Period 1:
• Focus on 2 applications:
a. container wagon with sensitive goods, and
b. tank wagon carrying hazardous goods;
• Definition of potential sensor system architectures;
• Dedicated solutions for the defined cases, concerning applicable sensors, required features and installation;
• Analysis of energy consumption to outline requirements for energy harvesters. Energy harvesters along with power management and storage systems are being designed (ongoing work):
- Adaptation of partner’s PER vibration energy harvesters (designed for passenger trains);
- RF-based energy harvesting and transfer solutions;
- Solar energy harvesting solutions;
• Designed, prototyped and tested in the lab and in relevant railway environment:
- The communication hub (GPS for tracking, GPRS for remote data transmission and Bluetooth for communication with sensors), along with
- Wireless sensor node (inc. pressure, temperature and humidity sensors);
• Connection and interface to an application server has been implemented and will be tested in rail freight operation;
• The RFID solution has been designed and prototype solutions are being tested in laboratory and on-site.

Work Stream 2: WAGON DESIGN

Scope: Development of a novel concept of modular and lightweight wagon.

Results achieved in Period 1:
• Focus on 3 potential options:
i. Flat wagon enabling container transport;
ii. Open self-discharge ‘hopper’ wagon for bulk materials;
iii. Cereals ‘hopper’ wagon;
• Analysis and selection of materials for case studies (input in lightweight design concepts);
• Design of lightweight solutions (ongoing work) through iterative phases for taking into account the results of structural strength and dynamic analyses:
- Y25 lightweight bogie (lightweight frame made of high strength steel, HSS, lightweight wheelset and optimised design);
- Lightweight modular wagon frame (made of HSS);
- Lightweight composite carbody (hopper);
• Modelling and analysis of lightweight structural solutions (ongoing work), to support iterations and refinement of design concepts.

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.

Results achieved in Period 1:
• Focus on 2 directions:
i. Condition Based Maintenance (CBM);
ii. Maintenance Management;
• Sets of real data from maintenance activities, alongside with bearing and wheelset failure data were analysed to work out:
- An estimate of the Life Cycle Cost (LCC) of the Y25 bogie, with a breakdown of costs on sub-systems;
- A reliability-driven analysis (RAMS analyses), resulting in the identification of the possible failure modes of key components and in the quantification of the corresponding failure rates.
• A prioritisation list was defined for the components of the Y25 bogie in terms of their relevance for predictive maintenance. The list of the critical components was then verified through a “case study”, in order to understand the benefits of implementing a predictive maintenance policy.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

WS1: Cargo condition monitoring
The intended advancement is to develop an autonomous, wireless and self-powered cargo condition monitoring system for monitoring the condition of cargo in railway environment. A detailed technical concept will be developed and validated through a demonstration in relevant environment. It will contribute towards the attractiveness of rail freight by enabling the provision of a service and capability (cargo condition monitoring, with tracking and tracing) which is not currently provided in a commercially viable form.
WS2: Wagon design
The expected advance beyond the state of the art for lightweight freight vehicle design is to develop innovative materials selection processes for assessing novel materials, and advanced lightweight designs of vehicle components to be validated through testing. The materials selection process will be applied along with optimisation techniques. A significant advance is that key aspects (e.g., the strength of joints between novel lightweight materials, fatigue properties, the environmental resistance of the materials, etc.) will be validated through testing.
WS3: Predictive maintenance
The development of a complete predictive maintenance system for selected freight vehicle components will be a significant innovation in rail vehicles maintenance. There are currently no predictive maintenance strategies that integrate the analysis of historical and condition monitoring data. The development and application of an assessment tool to evaluate the effect of adopting predictive maintenance will also be 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.

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