Ústav železničních konstrukcí a staveb

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Monitoring of Track Sections with Long-pitch Corrugation
(Czech Technical University in Prague, 2016-05-04) Valehrach, Jan; Šmíd, Jaroslav
The focus of this paper lies on monitoring of the track sections with rail corrugations caused by wheel sliding. Short waves on the running surface of the rail head on low rail are typical for this defect. Long-pitch corrugation is a significant cause of vibration and noise pollution in the railway infrastructure. Therefore it is very important to understand the formation and development of this imperfection of the rails. Measurements were carried out in curves of small radii on several tracks in the Czech Republic. In addition to the measurement of the surface of the rail head a number of supplementary parameters was evaluated for each section, such as curve radius, superelevation, track gauge etc. and the speed of passing trains as well. This paper describes rail corrugation defect and its development. Our early results are presented in the Conclusions.
Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data
(Hindawi, 2020-11-24) Krč, Rostislav; Podroužek, Jan; Floriánová, Martina; Vukušič, Ivan; Plášek, Otto
This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.
Autonomní diagnostika výhybek na základě snímání dynamické odezvy projíždějícího vozidla
(VTS pri Žilinskej univerzitě v Žilině, 2023-09-01) Raif, Lukáš; Navrátil, Petr; Vyhlídal, Michal; Plášek, Otto
Od roku 2020 probíhá vývoj autonomního zařízení určeného pro prediktivní diagnostiku výhybek a výhybkových konstrukcí v rámci projektu Výhybka 4.0 (TAČR, program Doprava 2020+). Principem je snímání signálů ze snímačů instalovaných na výhybce i na vozidle a následné zpracování signálů metodami strojového učení a umělé inteligence. Výsledkem je informace o stavu výhybky, která bude zprostředkována manažerovi železniční dopravní cesty. V článku byla představena obecná filozofie autonomní diagnostiky výhybek vyvíjená v rámci projektu Výhybka 4.0. Byly uvedeny obecné principy přístupu k této problematice a práce s daty. Podrobně byly představeny snímače a související zařízení pro subsystém, kdy infrastruktura kontroluje infrastrukturu, tj. DISC-S. Dále byly zevrubně popsány následující prvky celého systému, tedy databáze, vyhodnocovací software a zobrazení výsledků koncovému uživateli.
Sensitivity enriched multi-criterion decision making process for novel railway switches and crossings - a case study
(Springer, 2021-01-26) Boghani, Hitesh C.; Ambur, Ramakrishnan; Blumenfeld, Marcelo; Saade, Louis; Goodall, Roger M.; Ward, Christopher P.; Plášek, Otto; Gofton, Neil; Morata, Miquel; Roberts, Clive; Dixon, Roger
Background: Despite their important role in railway operations, switches and crossings (S&C) have changed little since their conception over a century ago. It stands now that the existing designs for S&C are reaching their maximum point of incremental performance improvement, and only a radical redesign can overcome the constraints that current designs are imposing on railway network capacity. This paper describes the process of producing novel designs for next generation switches and crossings, as part of the S-CODE project. Methods: Given the many aspects that govern a successful S&C design, it is critical to adopt multi criteria decision making (MCDM) processes to identify a specific solution for the next generation of switches and crossings. However, a common shortcoming of these methods is that their results can be heavily influenced by external factors, such as uncertainty in criterium weighting or bias of the evaluators, for example. This paper therefore proposes a process based on the Pugh Matrix method to reduce such biases by using sensitivity analysis to investigate them and improve the reliability of decision making. Results: In this paper, we analysed the influences of three different external factors, measuring the sensitivity of ranking due to (a) weightings, (b) organisational and (c) discipline bias. The order of preference of the results was disturbed only to a minimum while small influences of bias were detected. Conclusions: Through this case study, we believe that the paper demonstrates an effective case study for a quantitative process that can improve the reliability of decision making.
Design parameters of buffer stops
(Czech Technical University in Prague, 2016-10-30) Guziur, Petr
Paper discuses reasons of building buffer stops and situations that may occur in railway station leading to build safety tracks. Also discusses parameters of buffer stops that enter its design, such as collision speed and kinetic energy absorbing capacity. Furthermore, presents categories of buffer stops depending on principles of absorbing the kinetic energy and points pros and cons of each structure.

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