One of the objectives of the S2R Master Plan Innovation Programme 3 (IP3) “Cost efficient and reliable infrastructure” is to enable the development of autonomous and unmanned vehicles for railway network monitoring, by processing data captured by those devices in order to generate relevant maintenance infrastructure-related information. The specific challenge of such a concept relies on the technology development and demonstrator implementation in the field, to assess operational interests and feasibility in terms of which elements/parameters can be measured via unmanned aerial monitoring, and with which accuracy, to fulfil requirements of the specific applications.
The proposed research and innovation activities should address all the following elements, in line with the S2R MAAP (TD.3.7):
Analysis of the monitoring performances determined in other industrial fields, of various measuring systems based on satellites and/or Unmanned Aerial Vehicles (UAVs) implementation;
Identification, by using KPIs, of the key assets to be monitored and their related parameters and variables to be measured, for which the use of satellites and/or UAVs are particularly suited;
Selection of the most relevant monitoring system for each of the key assets to be monitored with satellites and/or UAVs;
Identification of the relevant methods for data processing and post-processing, required to monitor the relevant parameters and generate the required information;
Identification of gaps and/or barriers to be overcome for the usage of satellites and/or UAVs for asset monitoring, e.g. legal and security issues, technology improvement, etc.
The expected final output will include a technological proof in relevant environment (corresponding to TRL5) of aerial unmanned monitoring (satellites, UAVs, etc.) of key railway-related assets for which there is a clear and sustainable return of investments (to be developed). The prototype demonstration will have to generate maintenance infrastructure-related information for the railway maintenance processes, taking into account the application cases, which is planned to be developed under the activities of the complementary topic S2R-CFM-IP3-02-2016: Intelligent maintenance systems and strategies (In2Smart project). In particular, these application cases will include:
Monitoring of the ground movements nearby the infrastructure (due to human activities – working area nearby – or due to natural movements – landslides, mudflow). This use case will allow satellite monitoring and LIDAR measurements to be mixed.
Monitoring of hydraulic activities nearby the track (watershed, hydraulic gutter, track platform humidity level, etc.). This will allow multispectral, LIDAR and satellite monitoring to be mixed.
Identification of natural hazards (barrier, vegetation, etc.) which allow a global supervision of the infrastructure to be settled.
Electrical systems: monitoring device to be developed for hot spots localisation, corona effect, de-stranding detection, geometry control, etc.
Civil engineering structures (bridges) monitoring: maybe a radar interferometry technic with drone equipped with radar photo and multispectral analysis for cracks or movements detection
Safety monitoring (for maintenance impacts): identification of infra modification / deterioration due to human activities that could impact trains operation.
The prototype will also have to demonstrate which sensor performances, geo-referencing capabilities, costs, post-processing and analytics methods are used, to collect such maintenance infrastructure-related information
This work should be carried out considering that automation for acquisition but also for analysis of raw data obtained from UAVs and/or satellites is a critical issue: a large proportion of the acquired data will correspond to digital imaging, requiring dedicated post-processing methods that have to be completely automated to ensure effectiveness for the maintenance supervision. In this case, sensors and post-processing methods should be specifically worked out, notably for:
multispectral and hyperspectral optical imagery;
radar measurement (i.e. interferometry).
Moreover, in a context of multi-scale data acquisition the analysis process should propose optimal combinations of vehicles, sensors and post-processing methods to extract the most reliable and precise indicators for the studied application cases. The work should focus on:
permanently improving default identification;
automatically creating anomalies/faults/degradations databases;
enabling cross analysis (different kind of data) to improve anomalies/faults/degradations detection;
improving railway digital asset management.
The action expected to be funded from this topic will also be complementary to action carried out following the topic 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.
The activities are expected to contribute to:
Improvement of railway reliability and capacity thanks to a more effective maintenance management supported by the here developed monitoring solutions;
Improvement of the railway maintenance by the development of a highly integrated automatic system;
LCC reduction (lower physical complexity of systems, increased reliability and the like).
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 T.D.3.7 in line with the relative Planning and Budget.
Type of Action: Research and Innovation Actions