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Technologies for Sustainable & Attractive European Rail Freight

IP Coordinator: Norbert Kahl - DB

Overview


Topic:
S2R-OC-IP5-01-2019
Total Project Value:
€ 1 499 072,50
Duration:
from 01/11/2019 to 30/04/2022
S2R (Of H2020) co-funding:
€ 1 499 072,50
Coordinator:
Magno Santos
Evoleo Technologies LDA
Complementary projects:
Project website:

Objectives

The main objective of the LOCATE project is to replace as necessary as possible the preventive conditional or scheduled maintenance of mechanical parts of the bogie by predictive maintenance.

This goal will be achieved by overcoming some of the historical challenges of maintenance subject, by;

  1. Developing optimized condition-based maintenance strategies using dynamic tools locating and evaluating the impact on the such overall approach of CBM in maintenance planning and railway operations;
  2. Developing intelligent tools integrated for the operation of the overall maintenance policy: supporting localization of faulty components, maintenance scheduling and integrating maintenance operations tasks into daily services, while ensuring appropriate inventory control of stock and spare parts and assigning maintenance crew/ technicians according to their skills/competences;
  3. Testing and validating in practice an open architecture able to carry asset management data to the operator beyond the locomotive bogie, locating events and defects on freight wagons, track condition, etc.
  4. Developing a minimal digital twin for the bogie system, based on vehicle dynamic simulations and post-processing, while considering the local requirements of the system. In this way, providing a comprehensive methodology to derive minimal digital twins of complex mechatronic railway systems;
  5. Applying a cost-effective and reliability-based sensor design to locate defects and monitor structural integrity of critical and high cost components of the bogie following an in-depth analysis of freight-specific use cases;
  6. Improving overall competitiveness of freight rail transport, increasing the freight reliability and availability, and providing a shift from inspection activities and associated costs to cost-effective remote defect localization and monitoring solutions;

It is expected that a condition-based monitoring maintenance program will:
  • Increase of availability (concerns only the time to work on the bogie) 30%
  • Decrease of the costs (only the maintenance costs of the bogie) 20%
  • Increase of the reliability (of the bogies and the components linked) 60% (incidents per unit of route)

At the end of the project it will have developed tools and methods to
  • identify the failures in the bogies, primary and secondary suspensions, wheels, electric traction motor, or transmission. We will be able to anticipate these failures from several days to several weeks.
  • do pre-operational and operational planning using the data produced
LOCATE will have developed all the tools and components to perform a demonstration on a locomotive and in a maintenance depot environment, providing decision-making information to the operator. The technology readiness level will therefore be
TRL6 (technology demonstrated in relevant environment).

Results and Publications

D1.1 Gender Strategy Plan

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D1.2 Quality Assurance Plan

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D1.3 Data Management Plan

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D2.1 Use Cases Description

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D2.2 Report on Standard and Regulations

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D2.3 FMECA Analysis

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D2.4 Requirements and Architecture Specification

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D3.1 Available technologies assessment report

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D4.1 Available Models Assessment Report

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D4.3 Behaviour Prediction ,Simulation and Post Processing Results Report

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D5.1 Operational Constraints Identification Report

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D5.2. Monitoring and Thresholds Rules Specification

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D6.2 LOCATE Predictive Maintenance Framework

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D7.1 Dissemination and Communication Plan

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D7.2 Dissemination Report and Exploitation Plan

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D7.3 Final Brochure with Recommendations

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All deliverables, results and publications herewith provided reflects only the author's view and the S2R JU is not responsible for any use that may be made of the information it contains.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 881805