Abstract
The failure of railway axles can lead to catastrophic accidents, with the human and economic consequences that this entails. The vibratory performance of a freight train bogie is studied in this work aiming to identify the defects induced in the wheelset. The defects are generated mechanically with four severity levels. The bogie is tested in a roller rig test bench and vibration signals are recorded from sensors placed in the axle boxes of the wheelset. These signals are decomposed into several sub-signals using the Empirical Mode Decomposition. Then, the spectral power of these sub-signals is used as input for a Feedforward Neural Network to classify the vibration signals according to the defect level. The results show that the trained network can accurately identify the presence or absence of wheelset defects and their severity.
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References
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
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
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
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
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)
Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Soc. London A: Mathem. Phys. Eng. Sci. 454, 903–995 (1998)
Huang, D., Li, S., Qin, N., Zhang, Y.: Fault diagnosis of high-speed train bogie based on the improved-CEEMDAN and 1-D CNN algorithms. IEEE Trans. Instrum. Meas. 70, 1–11 (2021). https://doi.org/10.1109/TIM.2020.3047922
Rabah, A., Abdelhafid, K.: Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvolution. J. Vibroeng. 20, 240–257 (2018). https://doi.org/10.21595/jve.2017.18762
Jauregui-Correa, J.C., Morales-Velazquez, L., Otremba, F., Hurtado-Hurtado, G.: Method for predicting dynamic loads for a health monitoring system for subway tracks. Front. Mech. Eng. 8, 858424 (2022). https://doi.org/10.3389/fmech.2022.858424
Bustos, A., Rubio, H., Soriano-Heras, E., Castejon, C.: Methodology for the integration of a high-speed train in maintenance 4.0. J. Comput. Design Eng. 8, 1605–1621 (2021). https://doi.org/10.1093/jcde/qwab064
Zhao, Y., Guo, Z.H., Yan, J.M.: Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks. J VIBROENG. 19, 2456–2474 (2017). https://doi.org/10.21595/jve.2017.17238
Krummenacher, G., Ong, C.S., Koller, S., Kobayashi, S., Buhmann, J.M.: Wheel defect detection with machine learning. IEEE Trans. Intell. Transport. Syst. 19, 1176–1187 (2018). https://doi.org/10.1109/TITS.2017.2720721
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
Rilling, G., Flandrin, P., Gonçalves, P., Lilly, J.M.: Bivariate empirical mode decomposition. IEEE Signal Process. Lett. 14, 936–939 (2007). https://doi.org/10.1109/LSP.2007.904710
Goodfellow, I., Bengio, Y., Courville, A.: In: Deep Learning. MIT Press (2016)
Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Mach. Learn. 31, 1–38 (2004)
Acknowledgements
This publication is part of the R&D&I project MC 4.0, funded by AEI/10.13039/501100011033 via sub-projects PID2020-116984RB-C21 and PID2020-116984RB-C22. It is also funded by UNED via the project 2023-ETSII-UNED-06.
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Bustos, A., Rubio, H., Castejon, C., Garcia-Prada, J.C. (2024). EMD-Based Intelligent Crack Detection in Freight Railway Axles. In: Okada, M. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2023. Mechanisms and Machine Science, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-45709-8_79
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