Abstract
Wheel polygonalisation, as a common phenomenon in railway vehicles, will worsen the dynamic effect of wheel-rail and affect running safety. Detection of the polygonal wear is essential for railway vehicle maintenance and running safety. Therefore, a novel polygonal wear detection method based on vehicle vibration measurements is proposed in this paper. Firstly, the axle box vertical acceleration signal is decomposed into multiple intrinsic mode functions (IMFs) by the variational mode decomposition (VMD) algorithm. Then, the observed vibration signal composed of multiple IMFs is analyzed by the independent component analysis (ICA) algorithm, and the independent component related to polygonal wear is selected according to their correlation coefficients. Finally, the optimal independent component is used to calculate the order and amplitude of the polygonal wear by the inertia principle. To verify the effectiveness of the proposed method, the simulation signal and axle box acceleration signal of measured data are implemented. Experimental results demonstrate that the proposed method can effectively estimate the order and amplitude of the polygonal wear.
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Acknowledgments
The authors would like to thank the State Key Laboratory of Traction Power for providing equipment and materials to this project. The authors would also like to acknowledge the Xplorer Prize for sponsoring the project.
Funding
This work was supported by the National Natural Science Foundation of China (Grant No 51825504, U19A20110), the National Natural Science Foundation of China (Grant No. 52005416), the Sichuan Science and Technology Program (Grant No. 2020YJ0213).
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Xie, B., Chen, S., Wang, K., Yang, Y., Zhai, W. (2022). Polygonal Wheel Detection of Railway Vehicles Based on VMD-FastICA and Inertial Principle. In: Orlova, A., Cole, D. (eds) Advances in Dynamics of Vehicles on Roads and Tracks II. IAVSD 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-07305-2_14
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DOI: https://doi.org/10.1007/978-3-031-07305-2_14
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