Cost-Efficient and Reliable High-Capacity Infrastructure

IP Coordinator: Felicity Osborn - NR

Overview


Topic:
S2R-CFM-IP3-01-2019
Total Project Value:
€ 23 091 203,50
Duration:
from 01/12/2019 to 30/11/2022
S2R (Of H2020) co-funding:
€ 10 241 024,48
Coordinator:
Carlo Crovetto
Hitachi Rail STS
Complementary projects:
Previous Project:

Objectives

Starting from the objectives defined for and the results achieved in the IN2SMART project, IN2SMART2 is aimed at implementing specific demonstrators.
Main objectives for TD3.6 – Dynamic Railway Information Management System (DRIMS):

  • Asset status monitoring capabilities enrichment: automatic anomaly detection algorithms will allow discovering issues in a faster way;
  • Operational reliability increase (less service disruption): railway asset decay prediction will lead to more targeted maintenance interventions and fewer interventions due to sudden failures;
  • LCC reduction through condition-based maintenance of railway assets and continuous improvement of components/maintenance schedules.

Main objectives for TD3.7 – Railway Integrated Measuring and Monitoring System (RIMMS):
  • Increase of operational reliability (less service disruptions) and safety (less incidents) through continuous and integrated monitoring of railway assets and rolling stocks impact;
  • LCC reduction through condition-based maintenance of railway assets and continuous improvement of components/maintenance schedules;  
  • Safer and faster ways of monitoring the infrastructure assets based on innovative technologies;
  • Straightforward, automatic and continuous monitoring of railway infrastructure through the equipment of many in-service trains with low cost monitoring and processing components;  
  • Consideration of rolling stock impact on the railway infrastructure as a fundamental component in its overall monitoring.

Main objectives for TD3.8 – Intelligent Asset Management Strategies (IAMS):
  • Shift towards a tailor made maintenance approach by using the necessary tools for information management and decision support;
  • A scalable framework for asset management systems, containing the static and dynamic data from all relevant components of the rail infrastructure enabling improved lifecycle management, efficient maintenance strategies and adequate operations planning which includes logistic preparation, deployment of staff, tools, equipment and plant and possessions;
  • A holistic, system approach in combination with the new methodologies and data-driven concepts provided by TD3.6 and TD3.7;
  • Using LEAN thinking to design new working methods and tools making significant steps forward in reducing time needed for maintenance and cost.

Project Structure



WP01 - Grant Management – first phase

The objectives of this work package, relevant only to the first (M1-M18) phase of the project accordingly to guidelines on Lumps Sum projects, are to provide an: Effective coordination of the project; Efficient management of common consortium activities; Effective overall administrative and financial management of the project; Effective quality management; Effective management of Steering Committee meetings; Effective management of Technical Management Team meetings.



WP02 - Grant Management – second phase

The objectives of this work package, relevant only to the second (M19-M36) phase of the project accordingly to guidelines on Lumps Sum projects, are to provide an: effective coordination of the project; efficient management of common consortium activities; effective overall administrative and financial management of the project; effective quality management; effective management of Steering Committee meetings; effective management of Technical Management Team meetings.



WP03 - System Vision, Architecture & Validation

The objective of WP3 is to reach a holistic IAMS vision to guarantee coherence and uniformity to the IN2SMART2 TRL6/7 demonstrators implemented at WP level (from 4 to 15) and to validate it.

Therefore, WP3 will focus on: The refinement of the functional system architecture already identified in IN2SMART and the definition of the Open Standard interfaces between functional modules to be adopted by all use cases; The cooperation with the CFM S2R-CFM-IPX-CCA-01-2019 “System Architecture and CDM” and the identification and specification of the IN2SMART2 contribution to allow an adequate requirement traceability and implementation of the use case demonstrators; The harmonisation at WP level of asset related findings (e.g. tracks, track circuits, fasteners etc..); The definition of the target KPIs and the evaluation criteria for the demonstrators; The validation of the demonstrators according to the defined target KPIs and the evaluation criteria; The collection of lessons learnt and open issues after the evaluation of demonstrators; The cooperation with the S2R initiatives to ensure coherence with high-level KPIs; The application of CDM both within the individual demonstrators and at the ITD level; The definition of a common approach to IAMS Digital Twin; The demonstration of the IAMS integration potentialities through an integrated HMI - integrating the HMI/human factor elements developed in IN2SMART - supported by the joint use of the proposed system architecture, the Open Standard Interfaces and the CDM; The cooperation with other S2R projects with defined interface toward the IAMS by means of processes and data exchange, such as TMS and energy management system.


WP04 - Italian Urban Metro System IAMS: design and deployment

Starting from the findings of IN2SMART and in synergy with WP3, the objectives of WP4 are Tuning the generic architecture and functions for urban metro; Design and deploy a field installation covering mainly TD 3.7. The data collected will be the main input for the tactical and operational levels demonstrator in WP5; Design and implementation of Data Analytics platform and Decision Support framework covering TD 3.6 and TD 3.8.

The final goal will be minimising maintenance costs, optimising the use of resources while maximising network availability and reliability.

WP4 will be based on IN2SMART WP2 requirements and global functional system architecture for a IAMS ant its further evolution in IN2SMART2 WP3, IN2RAIL and IN2SMART developments towards a CDM and its further evolution in IN2SMART2 WP3. Other inputs will be IN2SMART signalling proxy developed in WP5; IN2SMART Weight in Motion, dynamic impact and wheel defects fiber optics based technologies, models and algorithms developed in WP6; IN2SMART data analytics algorithms of WP8 and IN2SMART decision support methodologies of WP9.

Therefore, WP4 will focus on: Definition of system requirements to be validated in WP5 and synthesised at WP3; Installation plan definition for on-board signalling devices, wayside signalling devices, vehicles’ parameters, data processing system; Equipment procurement and installation of monitoring and data collections systems; Data analytics design and first implementation of nowcasting, anomaly detection and forecasting; Decision Support framework design and links with data analytics; First setup and data collection of monitoring systems; Equipment procurement and installation of data processing system; First setup of data processing system.


WP05 - Italian Urban Metro System IAMS: In field validation

The objective of WP5 is to fine tune installation, integrate analytics platform and DSS framework, and validate a demonstrator at the tactical and operational levels covering all TDs (3.6, 3.7 and 3.8) aimed at minimising maintenance costs, optimising the use of resources while maximising network availability and reliability. WP5 will be based on WP4 results and continuous interaction with WP3.

Therefore, WP5 will focus on: Fine tuning and optimisation of monitoring and data collection systems installed in WP4; Deployment of a complete IAMS architecture and CDM data exchange in line with WP3; Implementation and testing of data analytic methods with a focus on nowcasting, anomaly and prediction of asset status based on multiple data sources for the same asset (looking at several different assets); Implementation and testing of Decision Support Methodologies and algorithms including scheduling (of teams and machinery) optimisation, integration of logistics aspects such as spare parts, multimodal transportation and dynamic HMIs adaptable to user needs; Overall system validation in line with WP3 defined methodology and KPIs.


WP06 - Digital twin for railway asset management: Application to the French and Spanish railway network

The objective of the WP proposed is to create digital twins of new track sections and a new catenary system section of the railway infrastructure, and to develop processes to register and digitalise their evolution as it is being built (called the “As Built Records”, or “ABR”). The use case will deliver a demonstrator at a TRL level of 7, i.e. a digital twin and the “as built records” of real works performed on the French and Spanish Railway Network.

The demonstrator will be carried out in 2022 on 2 segments of the SNCF-R railway according to the planning of the worksite scheduled in 2021, respectively, for the catenary: line Paris Nord – Lille, UIC class 3, segment between station “Gannes” and “Ailly Sur Noye”; for the track: line Arras – Dunkerque, UIC class 4, segment between station “Lens” and “Bethune”. It is expected to perform a Proof of the concept in 2020 before implementing the demonstrator in 2022. The level of TRL of the demonstrator is expected to reach 7. Additionally, a construction phase demonstrator will be carried out in Spain, in order to develop a streamlined methodology for the digitalisation of new assets not designed using BIM methodology.


WP07 - Anomaly detection for rail fastener systems

The objective for this WP is to develop a demonstrator at the operational level covering the TDs 3.6 and 3.7 aimed at minimising maintenance costs, optimising the use of resources while maximising network availability and reliability. More in detail, the UC will integrate (and whenever needed refine/further develop) the following IN2SMART developments: TD3.6: Analytics platform architecture data analytics algorithms for fastener anomaly detection; TD3.7: on-board mounted equipment for data collection on rail fasteners.

The WP is an expansion in size and technological challenges of technology developed and validated in the IN2SMART project using a state-of-the art eddy-current sensor mounted on the vehicle. The sensor is optimised to measure the surface of any conductive material below the sensor. The measurement is analysed by computing an algorithm developed to extract any anomaly for the rail fastener systems.

This WP will also develop a decision support data access system in accordance with the objectives for TD 3.8 by develop a cloud-based front end for operators to access appropriate decision support data for maintenance planning on the rail fasteners systems. The outcome of the WP will be tested and validated on site using in-service trains thus reaching TRL 6-7.


WP08 - Remote Condition Monitoring Maintenance Reduction Interventions and Decisions: Design and Deployment

The overall objective is to improve the quality of asset management decision making by identifying the asset alarms generated by outside factors, such as weather and maintenance activity to enable Operators to prioritise their workload and respond to alarms. This will ensure that serious issues are not lost in the ‘noise’ generated by a large number of simultaneous anomalous alarms and generate a better record of the performance of an asset, ensuring that Whole Life Costing Models utilise a fairer assessment of the reliability of assets, and providing additional knowledge for maintenance optimisation algorithms.

The objective of this work package is a development (in size and technological challenges) of a platform for the prototypes developed for Thales Use Cases from IN2SMART WP8 (Anomaly Detection and Compensation for Weather Effects) and WP9 (Remote Condition Monitoring Intervention Decision Support), related to TD3.6 and TD3.8 respectively. This platform will be loaded with historical data and validated against a subset of the data. The work will link with X2RAIL-2 WP6 as the data will be available in the Integration Layer and accessible via the Core Operator Workstation HMI. The work will also include the Points Fault Classifier Algorithm identified in WP6 of the Transforming Transport GA 731932 project. The work package will cover: The environment of Network Rail, specifically the Transpennine Route. The route section is a 122Km route from Manchester to York via Leeds in the North of England. The specific assets to be used on this route are to be determined with Network Rail but there are 304Km of track and 236 switches and crossings available; Performing big data analysis of historical data including raw points and track circuit data, weather data and all possession and fault management systems logs; Working with Infrastructure Managers to perform a big data analysis on the available data to develop the additional ‘Asset Inter-dependencies’ and ‘Alarms Due to Maintenance’ to create the inputs for the Fault Tree Model; Using historical and a live Weather data interfaces to associate weather stations to asset location.


WP09 - Remote Condition Monitoring Maintenance Reduction Interventions and Decisions: In Field Validation

The overall objective is to enhance the Platform, as specified and tested in WP8, to create a Real World demonstrator (TRL 6) through connection to live data collection sources for a minimum period of 12 months.

The scope of the Prototype is at the operational level (IAMP) covering all TDs (3.6, 3.7 and 3.8) and is aimed at reducing the number of Track Circuits alarms that are raised anomalously due to weather effects and reducing the number of alarms raised due to maintenance work. These outputs will be available in the S2R Integration Layer and displayed as asset status information, prioritised lists, alarm views and KPI views within the Core Operator Workstation (X2RAIL2 WP6). The data is also available via the Integration Layer for use in the Tactical Level (AMP, SAMP) by providing additional asset knowledge for maintenance optimisation algorithms. The work package shall cover the following work on the platform: Load with historical data and the algorithms tested and validated against the full historical data; Connect to live data sources for raw points and track circuit data, weather data and all possession and fault management systems; Combine the previously processed historical data with the live data feeds to collect data for up to 12 Months, with regular monitoring and analysis of the results to provide additional and improved asset knowledge for the maintenance optimisation algorithms; For the identified section of Network Rail infrastructure, the team responsible for scheduling, fault finding, and maintenance will be engaged with, with the aim to understanding and quantifying the potential impact this prototype may have in the real world.


WP10 - SMART maintenance on rail freight corridor Rhine-Alpine

This WP will continue the work of IN2SMART and will include results of the OC Asset4Rails. The goal is to demonstrate condition-based maintenance and asset management on real sections of the Rhine-Alpine Rail Freight Corridor (RFC1).

WP 10 focuses on: Creation of a digital description of the track (digital twin) in terms of design, components, condition (historical /actual), defects, maintenance, behaviour, load, environment and models; Monitoring and inspection of the track and S&Cs; Development and application of data analytics for anomaly detection, quality management, identification and classification of defects, root cause analysis and prediction of degradation; Development and application of decision support tools for LCC and RAMS based decision including maintenance, renewal and improvements; Maintenance execution including lean tamping and combined maintenance activities; Comparison and assessment of different maintenance or asset management strategies.


WP11 - Integrated Asset Management for Civils

The objective of WP11 is to extend one use case developed and validated in IN2SMART (D8.2 Section 4.3 Anomalies in Track Geometry Degradation) in addition to another developed through Transforming Transport GA 731932 (Track Interface Tamping), with the aim of integrating these UC components into a decision support system at operational, tactical and strategic levels for civils assets: Machine Learning models of track geometry degradation and tamping effectiveness have been deployed in a TRL 3 “Tamping Planning Concept”.  This displays the current track geometry condition and by employing predictive models identifies priority tamping operations in a 1-2-year time window. The IN2SMART2 Tactical Demonstrator would support identification of the most appropriate maintenance action for track geometry (tamp/stone-blow) and enable the formulation of prioritised plans.  Crucial drivers around network access will be explored.  The Demonstrator will align with industry KPIs classed as“Good-Track-Geometry” (GTG) and “Poor-Track-Geometry” (PTG).  The objective is a maintenance plan that sustains track geometry condition metrics;IN2SMART also developed a life cycle engineering (LCE) prognosis tool providing short/medium forecasts (12 - 50 years) maintenance and reinvestment/replacement measures and costs for the existing bridges of Wiener Linien (WL). Different maintenance strategies, preventive maintenance versus “do minimum”, were analysed and evaluated. The IN2SMART2 Strategic Demonstrator will include more in-depth budget analysis and calibrate degradation models. Key objectives will be: To establish the optimum intervention plan for a given budget (‘design to cost’); better integration of the tool with established WL information systems.

WP12 - Operational Asset Management in a Dutch environment

This work package is an integrated demonstrator showing the operational asset management in a real life demonstration in a Dutch environment. Work is based on individual building blocks started in IN2SMART. It shows, based on some examples, the integrated approach how to come from data acquisition, data analytics and decision support to the actual logistical execution of the interventions. Strukton Control Centre is an essential linking pin between monitoring the network and the daily practice and execution of the work. Scope of the integrated demonstrator is the tactical and operational levels, with a strong focus on the latter, in the asset management process, covering all TD’s (3.6, 3.7 and 3.8) of the intelligent maintenance pillar.

The more specific objectives are:
Data Acquisition: within this work package the following data sources will be developed/used: improved switch engine current measurements; local measurements (e.g. temp, vibration), provided by IoT sensors; video images of track; ABA (Axle Box Accelerators); additional data sets (e.g. provided by the flexible measurement unit under development), Sensor data fusion will further enhance the data quality;

Data analytics: Development and Application of improved deterioration / prediction models of specific assets such as Rail, Rail Joints, Railway Switches etc. Extension and further optimisation of the approaches developed in IN2SMART for data-driven anomaly detection, the development of transparent diagnostic models and asset status predictions based on (un)supervised approaches; Aim is to automate inspection by development of enhanced tooling for analysis of train bound and wayside datasets in order to reduce/eliminate the need for field inspection (foot patrol);

Decision Support: based on defining the severity / risk assessment and translation into maintenance actions; Planning of maintenance actions (logistics) and planning of work in general (capacity) in a service organisation.


WP13 - Robotic and automated LEAN execution

In IN2SMART WP10, a concept demonstrator (TRL 4) of a robotic platform for maintenance was developed. The objective of WP13 is to use this work to further develop the robotic command and control aspect to TRL 7.

It will be demonstrated to command and control a small wheeled vehicle with robotic maintenance capability, designed to operate during track possessions (no service trains running). This will be extended to an example for a specific use/application (end effector) for the robot. The example chosen is a high-pressure waterjet cutting machine.


WP14 - Track maintenance decision support tool for a Swedish heavy haul railway line

The main objective of this work package is to develop a decision support tool for railway track maintenance. These objectives can be divided into the following sub-objectives: To collect, measure and detect the track status and precursors of track degradation based on work IN2SMART WP3, WP4, WP6 and WP8; To integrate the prediction model of the behaviour of track degradation for segments developed in IN2SMART WP8 with predictive models with the possibility to including isolated defects; To implement and adopt the integrated RAMS, LCC and Risk framework for generating maintenance plans for maintenance schedules, previously developed in IN2SMART WP9; To define specifications and requirements for an Integrated Maintenance Decision Support Platform and to incorporate the human factor guidelines developed in IN2SMART WP2.

WP15 - Strategical and tactical track asset management for the North Line in Portugal & New Tram Depot Design with smart IAMS approach

Starting from the findings of IN2SMART the objectives of WP15 are: Design and implementation of a data analytics platform using already developed algorithms (from Past EU projects, IN2SMART, IM degradation models in use, etc.) for track degradation models; Design and implementation of a detailed tactical mid-term maintenance and renewal planning tool with focus on track assets. It will be based on the North Line in Portugal, managed by IP, with a length of 336,079 km connecting Lisbon and Oporto, acting as the main North-South corridor; Design and implementation of a strategical decision support tool based on the tactical planning tool (simulation-based approach) to support the assessment of the IM asset management strategic KPIs; Development of a standard depot design fitted to Zaragoza’s tram concept, including the definition of basic requirements, sizing of elements and spaces, as well as a detailed design and technical specification of the building. This information, fed by de data collection from infrastructure, will be included as rules in BIM methodology for depots designs, with the aim of get a basis design to be customise afterwards to the real case; Based on simulation the defined target KPIs will be evaluated and lessons learned are collected. WP15 will be based on IN2SMART data analytics algorithms of WP8, decision support methodologies of WP9 and good practices for smart depot design as use case in WP9 as well as the requirements and global functional system architecture for a IAMS of WP2 and its further evolution in IN2SMART2 WP3.

WP16 - Dissemination, Communication and Exploitation

In alignment with the dissemination and promotion activities of the S2R JU the WP will guarantee a proper dissemination and promotion of the project and its results ensuring that all important actors in the European railway sector are consulted and informed about IN2SMART2 objectives, contents and results



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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 881574