|
|
Civil-Comp Conferences
ISSN 2753-3239
CCC: 7
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON
RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Paper 7.15 Smart Autonomous Diagnostics of Switches and Crossings L. Raif 1, O. Plášek 2, M. Kohout 3, V. Salajka 4, J. Podroužek 5, J. Vágner 3, A. Hába 6, P. Navrátil 1, M. Vyhlídal 1, I. Vukušič 2, R. Krč 5 and Z. Hadaš 71R&D, DT - Vyhybkarna a strojirna, a.s., Prostějov, Czechia
2Institute of Railway Structures and Constructions, Faculty of Civil Engineering, Brno, Czechia
3Department of Transport Means and Diagnostics, Faculty of Transport Engineering, Pardubice, Czechia
4Institute of Structural Mechanics, Faculty of Civil Engineering, Brno, Czechia
5Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno, Czechia
6Department of Mechanics, Materials and Machine Parts, Faculty of Transport Engineering, Pardubice, Czechia
7Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno, Czechia Full Bibliographic Reference for this paper
L. Raif, O. Plášek, M. Kohout, V. Salajka, J. Podroužek, J. Vágner, A. Hába, P. Navrátil, M. Vyhlídal, I. Vukušič, R. Krč, Z. Hadaš, "Smart Autonomous Diagnostics of Switches and Crossings", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on
Railway Technology: Research, Development and Maintenance",
Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 7, Paper 7.15, 2024, doi:10.4203/ccc.7.7.15
Keywords: permanent-way, switches & crossings, autonomous diagnostic, artificial intelligence, machine learning, structural health monitoring.
Abstract
This paper shows the results of the Turnout 4.0 applied research project. The project focused on the development of an autonomous diagnostic system. A modular diagnostic system is described, including a sensor part installed on the crossing and the vehicle, data acquisition and storage in database systems, evaluation of signals by two systems – Train Identification System and Diagnostic Switches & Crossings – using artificial intelligence algorithms, Machine Learning and Bayesian statistical analysis. The extension of the system is the determination of the final score, describing the technical condition of the crossing, evaluating the score development over time, and displaying the monitoring results to the infrastructure manager.
download the full-text of this paper (PDF, 13 pages, 1105 Kb)
go to the previous paper
go to the next paper
return to the table of contents
return to the volume description
|
