Selected aspects of the diagnostic process in rail transport

• Autorzy: Chrzan Marcin , Ciszewski Tomasz , Nowakowski Waldemar

Identyfikatory

DOI

10.53502/RAIL-175048

• Języki publikacji: EN

Abstrakty

EN

The article presents selected results of research in the area of reducing the risk of defects in railway infrastructure and traffic control devices. The first part of the article will discuss selected topics used in a defectoscope car for automated ultrasonic rail inspections related to the identification of joints and flaws. A method based on the identification of joints and flaws using a neural network will be presented. The second part of the article will cover research on the automatic collection of diagnostic data from railway traffic control devices. The solutions presented concern a simulator of railway traffic control device malfunctions, from which data is extracted to populate a database of malfunctions and then used in the inference process. The article will present partial results of research on both systems.

Słowa kluczowe

EN

defectoscope wagon; safety; diagnostics; inference

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Pojazdy Szynowe
• Rocznik: 2023
• Tom: Nr 3-4
• Strony: 3--12
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Chrzan Marcin

• Faculty of Transport, Electrical Engineering and Computer Science, Radom University, Radom, Poland

• https://orcid.org/0000-0003-4181-4847

autor

Ciszewski Tomasz

• Faculty of Transport, Electrical Engineering and Computer Science, Radom University, Radom, Poland

• https://orcid.org/0000-0002-5493-2116

autor

Nowakowski Waldemar

• Faculty of Transport, Electrical Engineering and Computer Science, Radom University, Radom, Poland

• https://orcid.org/0000-0003-0592-9065

Bibliografia

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• [2] Bojarczak P, Nowakowski W. Application of deep learning networks to segmentation of surface of railway tracks. Sensors. 2021;21(12):4065. https://doi.org/10.3390/s21124065
• [3] Burdzik R, Konieczny Ł, Nowak B, Rozmus J. Research on vibration employed for the train traffic control. Vibroengineering Procedia. 2018;14:227-232. https://doi.org/10.21595/vp.2017.19237
• [4] Ciszewski T, Nowakowski W, Bojarczak P. Application of RAMS methodology to computerized railway systems. Transport Means. 2022;1:212-217.
• [5] Ciszewski T, Nowakowski W, Chrzan M. RailTopoModel and RailML – data exchange standards in railway sector. Archives of Transport System Telematics. 2017;10(4):10-15.
• [6] Chen W, Liu W, Li K, Wang P, Zhu H, Zhang Y et al. Rail crack recognition based on adaptive weighting multiclassifier fusion decision. Measurement. 2018;123:102-114.https://doi.org/10.1016/j.measurement.2018.03.059
• [7] Chen Z, Wang Q, Yang K, Yu T, Yao J, Liu Y et al. Deep learning for the detection and recognition of rail defects in ultrasound B-scan images. Transp Res Rec. 2021: 036119812110215.https://doi.org/10.1177/03611981211021547
• [8] Drózd P, Rosiński A. Increasing the readiness of railway traffic control devices using a functional test generation method. Appl Sci. 2023;13(13):7717. https://doi.org/10.3390/app13137717
• [9] Gołabek P, Madej L. Kwalifikacja zapisów B-scan z ultradźwiękowej badania szyn kolejowych za pomocą modelu wytrenowanego w trybie głębokiego uczenia. Przegląd Elektrotechniczny. 2019;1(12):119-22. https://doi.org/10.15199/48.2019.12.24
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• [11] Liu X, Lovett A, Dick T, Rapik Saat M, Barkan CP. Optimization of ultrasonic rail-defect inspection for improving railway transportation safety and efficiency. J Transp Eng. 2014;140(10):04014048.https://doi.org/10.1061/(asce)te.1943-5436.0000697
• [12] Nowakowski W, Ciszewski T, Lukasik Z. Application of logical diagnosis to identify faults in computerized railway traffic control systems. In: Ginters E, Ruiz Estrada M, Piera Eroles M. (eds). ICTE in Transportation and Logistics 2019.ICTE ToL 2019. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-39688-6_16
• [13] Sadeghi J, Rahimizadeh Y, Khajehdezfuly A, Rezaee M, Rajaei Najafabadi E. Development of rail-condition assessment model using ultrasonic technique. J Transp Eng A. 2020;146(8):04020078.https://doi.org/10.1061/jtepbs.0000390
• [14] Shamanov V. Formation of interference from power circuits to apparatus of automation and remote control. Proceedings of 16th IEEE East-West Design & Test Symposium (EWDTS‘2018). 1218:140-146.https://doi.org/10.20295/2412-9186-2022-8-03-252-265
• [15] Sulimova V, Zhukov A, Krasotkina O, Mottl V, Markov A. Automatic rail flaw localization and recognition by featureless ultrasound signal analysis. 14th International Conference, MLDM 2018. Cham, Switzerland, Springer. https://doi.org/10.1007/978-3-319-96136-1
• [16] Wu F, Li Q, Li S, Wu T. Train rail defect classification etection and its parameters learning method. Measurement. 2020;151:107246. https://doi.org/10.1016/j.measurement.2019.107246
• [17] Zabielska A, Jacyna M, Lasota M, Nehring K. Evaluation of the efficiency of the delivery process in the technical object of transport infrastructure with the application of a simulation model. Eksploat Niezawodn. 2023:25(1).http://doi.org/10.17531/ein.2023.1.1

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).