Wheel Damage Prediction Using Wayside Detector Data for a Cross-Border Operating Fleet with Irregular Detector Passage Patterns

Author:

Öhman Johan,Birk Wolfgang,Westerberg Jesper

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Alemi A, Corman F, Lodewijks G (2017) Condition monitoring approaches for the detection of railway wheel defects. Proc Inst Mech Eng, Part F: J Rail Rapid Transit 231(8):961–981

2. Zeng Y, Song D, Zhang W, Hu J, Zhou B, Xie M (2021) Physics-based data-driven interpretation and prediction of rolling contact fatigue damage on high-speed train wheels. Wear 484:203993

3. Birk W, Dittman T, Karim R, Westerberg J (2019) Experiences from the detection and prediction of wheel damages on railway vehicles in operation. In: Proceedings of the 13th international heavy haul association STS conference. Narvik

4. Mohammadi M, Mosleh A, Vale C, Ribeiro D, Montenegro P, Meixedo A (2023) An unsupervised learning approach for wayside train wheel flat detection. Sensors 23(4):1910

5. Karim R, Birk W, Larsson-Kråik PO (2015) Cloud-based maintenance solutions for condition-based maintenance of wheels in heavy haul operation. In international heavy haul association: the 11th international heavy haul association conference will be held 21–24 June 2015 in Perth 21/06/2015–24/06/2015. International Heavy Haul Association

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