EMD-Based Intelligent Crack Detection in Freight Railway Axles
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45709-8_79
Reference16 articles.
1. Bernal, E., Spiryagin, M., Cole, C.: Onboard condition monitoring sensors, systems and techniques for freight railway vehicles: a review. IEEE Sensors J. 19, 4–24 (2019). https://doi.org/10.1109/JSEN.2018.2875160
2. Li, Y., Liang, X., Chen, Y., Chen, Z., Lin, J.: Wheelset bearing fault detection using morphological signal and image analysis. Struct Control Health Monit. 27, (2020). https://doi.org/10.1002/stc.2619
3. Li, C., Luo, S., Cole, C., Spiryagin, M.: Bolster spring fault detection strategy for heavy haul wagons. Veh. Syst. Dyn. 56, 1604–1621 (2018). https://doi.org/10.1080/00423114.2017.1423090
4. Bernal, E., Spiryagin, M., Cole, C.: Wheel flat detectability for Y25 railway freight wagon using vehicle component acceleration signals. Vehicle Syst. Dynam. 1–21 (2019). https://doi.org/10.1080/00423114.2019.1657155
5. Bustos, A., Rubio, H., Meneses, J., Castejon, C., Garcia-Prada, J.C.: Crack detection in freight railway axles using power spectral density and empirical mode decomposition techniques. In: Uhl, T. (ed.) Advances in Mechanism and Machine Science. IFToMM WC 2019. pp. 3691–3701. Springer International Publishing, Cham (2019)
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