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Smart metering and asset management of railway systems - S2R-OC-IP3-01-2017
Deadline: 30 Mar 2017   CALL EXPIRED

EU logo mono EC - Horizon 2020

 Energy Efficiency
 IT
 IT Applications
 Transport
 Urban transport
 Horizon2020
 Open Source
 Pollution

Specific Challenge: 

In the framework of the S2R Innovation Programme 3 (IP3) “Cost efficient and reliable infrastructure”, of the S2R Master Plan, there are, inter alia, two Technological Demonstrators (TDs) in the S2R MAAP aiming at generating knowledge from data: 

 “Smart Metering for Railway Distributed Energy Resource Management System” (TD3.10), where the specific challenge relies on the technology development and demonstrator implementation in order to realise: 

o A non-intrusive Smart Metering sensor networks at Railway System level. 

o An open system and interface for data collection, aggregation and analysis in an open source Operational Data Management (ODM) Platform. 

o A set of User Applications design and specifications. The Applications will exploit the energy analysis process with the aim of enhancing the energy decision making and the line operation patterns, as well as other possible improvements such as preventive maintenance. 

“Dynamic Railway Information Management System (DRIMS)” (TD3.6) where the challenge is to generate knowledge from data and/or information – driven whenever necessary by the available domain knowledge - valid for life cycle management and intelligent asset maintenance planning including automatic detection of anomalies, discovering and describing the maintenance workflow processes and implement predictive models of decaying infrastructural assets. 

 

Scope: 

The proposed research and innovation activities should address both work streams described below, in line with the Shift2Rail Multi-Annual Action plan (MAAP): 

• Work-stream 1: Management of energy related data 

The activities regarding this work stream should cover all following elements: 

1. Development of sensors for railway systems energy data collection and transmission from the field, as well as technical support for the aims of TD 3.10 installation. This should include: 

o Analysis of existing components, systems and solutions that can be adapted to the railway environment for the measurement of energy and environmental related values. For example, analysis of smartphone or other low cost communicating sensors for on-board and track side energy measurements. 

o Technological development of programmable smart sensors ready to be adapted in railway environment, on-board and trackside. 

o Development of technologies for underground train geographical location and accurate train kinematic parameters measurements. These measurements will be integrated in a Geographic Information System (GIS) solution and synchronised with energy related measurements. 

o Development of advanced methods for underground equipment cartography, such as ground LIDAR (Light Detection and Ranging) combined with GIS. 

 

o Development of data recorders, relays and concentrators to be used on-board or at ground. 78 

 

The activity is expected to finish with a technological proof of sensors and data transmission components, validated in relevant environment (TRL5). The sensors and the associated electronic modules to be delivered at the end of the project should have the following properties/characteristics: 

• Be self-powered, low energy, with wireless transmission capabilities and able to be used in tunnels, accompanied by detailed technical specifications for their physical installation in the railway environment. 

• Be able to perform measurements of energy (voltage, current), motion, localization and environmental parameters (temperature, CO2, pollution, noise). 

• Support Network Time Protocol (NTP) for data time stamping. 

• Have resilient telecommunications capabilities adapted to railway environment in tunnels and at surface. 

• Support intelligent algorithms for data prioritization and data transmission management. 

2. Energy data management architecture research for the TD3.10 data collection, processing and storage, including the following software specifications and developments: 

o Analysis of existing frameworks, systems and solutions that can be adapted to the railway applications environment for the management of energy related data, including GIS. 

o Specification and application of the most appropriate data distribution strategy within the data infrastructure, stating how data is acquired by the system, where is it processed, and how it is aggregated. 

o Develop scalable solutions for the data processing architecture for handling large, heterogeneous data volumes in short times, such as data compression algorithms, etc. 

o Develop adapted Data Storage solutions for a distributed and rapid data access, able to process streams of continuous data, as required by real-time sensing applications. 

o Develop techniques for securing data such as encrypted databases and the associated secured analytic engines. 

o Develop interfaces with railway control systems such as SCADA, etc. dedicated to railway operational data integration in the overall processing flow. 

o Develop the software for human machine interface for operational purposes such as data visualization and big data management. 

 

The activity is expected to finish with a prototype of an Operational Data Management (ODM) Platform and the associated software validated in relevant environment (TRL5). The data management software to be delivered at the end of the project should have the following properties/characteristics: 

• Continuous data collection from meters and installed sensors as well as associated streaming data processing. 

• Non-intrusive and loosely-coupled data transmission protocols such as message-oriented middleware. 

• Ability of processing data provided by heterogeneous sources and in large volumes. 

• Data formatting and reconciliation for ensuring the synchronization of captured data at different times. 

• Data processing capability for removing non-relevant data (odd-blank-inconsistent). 

• Analytics capabilities and specific algorithms for data-knowledge extraction. 

• Elasticity of the data management architecture in order to be incremented by new nodes when the current configuration needs more performance. 

 

• Open source framework. 

 Be able to interface with other possible data collection platforms such as GIS platforms, etc. and other possible data analytic engines. 

 

3. Energy user applications development and associated modelling research applied to the TD3.10. The following Application domains should be investigated: 

o Data extraction and data visualization applications and integration in a GIS framework. Data coherence algorithms development. 

o Public portal applications for enhanced customer experience: dedicated portals or applications will provide specific energy data and contribute to energy savings by highlighting travel energy footprint, proposing customer travel habits changes, etc.. 

o Railway system modelling application development in conjunction with measurement inputs. Embedded learning algorithms for energy estimation from train kinematic parameters. 

o Predictive maintenance applications using energy measurements. 

o Demonstrator specific energy market interface applications such as demand response, frequency regulation, or any other specific applications. 

o Railway asset management applications. 

The activity is expected to finish with a prototype of the user applications described above, validated in relevant environment (TRL5). 

The activities under work stream 1 are expected to request a contribution of indicatively € 1.4 million. 

 Work-stream 2: Management of asset related data 

 

The activities regarding this work stream should address the necessary IT solutions and related methodologies for business security, economic sustainability and decision support in the field of big data and analytics railway applications in the field of asset management, covering: 

a. IT solutions for data and transactions security and safeguarding data ownership rights. 

b. Methodologies and related IT solutions for the extraction of (visual or rule-based) explicit knowledge from data-driven models, exploitable by decision makers to interpret phenomena underlying analytics algorithms. 

c. Study and proof-of-concept on the metrics and methods/tools to measure the accuracy of analytics algorithms. 

d. Study and proof-of-concept on the railway specific structural contract mechanisms for information and knowledge exchange in order to guarantee a proper management of the value of the information dealt with, and the exploitation of general accounting services. 

 

The activity is expected to request a contribution of indicatively € 0.8 million in order to deliver a validation of the activities described in points a and b above in relevant railway environment (corresponding to TRL5) and to provide an experimental proof of concept (corresponding to TRL3) of the studies described in points c and d above. 

The action that is expected to be funded under this topic will be complementary to the action that is expected to be funded under the topic S2R-CFM-IP3-01-2017: Smart Systems for Energy Management and Future Stations Solutions 

 

The action expected to be funded from this topic will also be complementary to actions carried out following the topics: 

 S2R-CFM-IP3-02-2016: Intelligent maintenance systems and strategies. 

 

As specified in section 2.3.1 of S2R AWP for 2017, in order to facilitate the contribution to the achievement of S2R objectives, the options regarding 'complementary grants' of the S2R Model Grant Agreement and the provisions therein, including with regard to additional access rights to background and results for the purposes of the complementary grant(s), will be enabled in the corresponding S2R Grant Agreements. 

An indicative scheduling of the deliverables is suggested below45: 

45 The scheduling of the deliverables is provided to facilitate the complementarity with the CFM actions and it is not binding. Additionally, each deliverable may have some flexibility in the scheduling. 

 Deliverables of work stream 1 are expected to be available as specified below: 

 For element nr 2.: by month 18; 

 For elements nr 1. and 3.: by month 24; 

 Deliverables of work stream 2 are expected to be available by month 24. 

 

Expected Impacts: 

An analysis of the positive impacts of smart metering for energy management purposes can be highlighted for several cases specific to the main rail transportation energy consumption profiles, for example: 

 Detailed knowledge of energy flows and consumers behaviour inside the railway system. 

 Energy profile prevision improvement. Refined knowledge of the traffic disturbances and consequences on system’s energy profiles. 

 Continuous supervision of power supply equipment status. Improved reliability and LCC based on predictive maintenance by continuous supervision of energy consumption and early identification of the abnormal variations. 

 Optimized return of investment (ROI) and ability for developing a better business plan, allowing optimal investments and asset management. 

 Better identification of electric infrastructure losses. 

 Better identification of auxiliary, maintenance facilities and stations energy consumption and of opportunities for savings and for demand-response. 

 Increasing the power supply quality and optimizing the line capacity. 

 Better coordination between the energy hourly variation prices and the traffic operation. 

 Valorisation of braking energy 

 Better management of train lighting and air conditioning/heating when not in revenue service. 

 

A significant impact is expected from the methodologies and tools for business security, economic sustainability and decision support in the field of big data and analytics railway applications on the following areas: 81 

 

 

 Improvement of capacity – a large improvement in line capacity due to a more effective asset maintenance management; 

 Improved Reliability: failure modes of current systems will be reduced/eliminated due to the new “intelligent asset management”; 

 Improved safety – as a consequence of the improved reliability, the number and magnitude of incidents will be reduced; 

 Significant LCC savings should be possible, due to new asset management approach. 

 

Specific metrics and methods to measure and achieve impacts should be included in the proposals, with the objective to achieve by the end of the S2R Programme the quantitative and qualitative targets defined in the S2R MAAP related to TD3.6 and TD3.10 in line with the relative Planning and Budget. 

Type of Action: Research and Innovation Actions 



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