1st TRIMIS Horizon Scanning Session

The Transport Research and Innovation Monitoring and Information System (TRIMIS) is an open-access transport policy-support tool developed and managed by the Joint Research Centre (JRC) to support the implementation of the Strategic Transport Research and Innovation Agenda (STRIA). One of the main objectives of TRIMIS is to provide a forward-oriented support to transport research and innovation (R&I) governance by using foresight in its technological and socioeconomic assessment process related to transport R&I. Within the TRIMIS framework, horizon scanning is applied through a structured and systematic collaborative exercise that contributes to the identification of new and emerging transport-related technologies and trends, with a potential future impact on the transport sector. Furthermore, it supports the assessment of current and future research needs and provides transport related insights to the broader European Commission foresight system contributing to a higher-level strategic framework also covering the transport domain. As part of this process, on 26 September 2019 the TRIMIS team, with support from the Unit for Knowledge Management and the EU Policy Lab of the JRC organised a sense making session entitled the 1st TRIMIS Horizon Scanning Session. It aimed at gathering insights from various transport experts with different backgrounds and make sense of previously collected, transport-related horizon scanning items through a process that could provide indications on relevant trends, new drivers of change, weak signals, discontinuities or shocks/’wild cards’/sudden unexpected events/’black swans’. This report collects and analyses the experiences that were shared and discussed during the session along with the supplementary material and initial results. Furthermore, it acts as a first input to the next step of the TRIMIS Horizon Scanning process that will involve policymakers with a focus on transport R&I.JRC.C.4-Sustainable Transpor

Assessment of Railway Train Energy Efficiency and Safety Using Real-time Track Condition Information

This paper presents the use of track condition data from the virtual remote wireless sensor network within a simulation model of a battery-hybrid diesel-electric locomotive-driven freight train for a realistic mountain railway route simulation scenario. Simulation model includes the point-mass model of freight train longitudinal motion dynamics subject to wheel-to-track adhesion and head wind variations, the model of hybrid diesel-electric locomotive energy efficiency, and the model of real-time information provide to the virtual train driver about railway track conditions based on a narrow-band wireless remote sensor network. Simulation results are used to assess the possible benefits remote wireless sensor data for freight train energy-optimal control and to increase the transportation safety, including prediction of possible delays due to changed weather conditions en route

Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data

With the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection. This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model

Fuzzy Integral Based Multi-Sensor Fusion for Arc Detection in the Pantograph-Catenary System

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier

Pantograph arc location estimation using resonant frequencies in DC railway power systems

Pantograph arcing in electrified railway systems not only reduces the power collection quality of a locomotive but can also damage pantograph strips and overhead lines (OHLs). Most research detects pantograph-to-OHL arcs based on onboard voltage/current measurements, pantograph cameras, and so on. The use of onboard voltage/current data, though being cost-effective, rarely reflects arc locations along OHLs. This article develops an arc positioning method, which matches the position-dependent resonant frequency (RF) of an OHL with the RF extracted from voltage measurements in a pantograph arc event. A particular 20-km DC railway line supplied by two substations is first modelled in MATLAB/Simulink, with the model effectiveness being assessed based on voltage measurements in an arc event. Then, the OHL-related RFs estimated by the model are validated by the Tableau formula and discussed alongside impacts on RFs based on line models, locomotive locations, and line lengths. These evaluations permit the generation of an RF curve that links OHL-related RFs with arc locations. The arc positioning method is tested based on the pantograph arc events presumed at various positions along the 20-km line, showing errors within 0.2 km at certain locations. The ability to determine arc locations will permit periodic inspections to be performed on the determined line sections

A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade