Dynamic analysis and detection of wheel polygonization on high-speed trains based on axle box vibrations

  • Xiaoyue Qi State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
  • Yuanchen Zeng State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
  • Dongli Song State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
  • Weihua Zhang State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
  • Bin Zhou Technology Center for Operation and Maintenance of High-speed Railway, China Railway Shanghai Group Co. Ltd. Shanghai, China
  • Mingyuan Xie Technology Center for Operation and Maintenance of High-speed Railway, China Railway Shanghai Group Co. Ltd. Shanghai, China
Keywords: Wheel polygonization, Condition monitoring, Axle box vibration, Vehicle dynamics, High-speed train

Abstract

Wheel polygonization of high-speed trains has received considerable attention because it deteriorates the dynamic interaction of wheel and rail and intensifies the vibration and noise of the vehicle-track system. The dynamics of the vehicle system, the excitation of track irregularities, and the movement and deformation of wheelsets introduce extensive coupling and complexity to the vibration response of high-speed trains, especially in medium and high-frequency ranges. The paper establishes a multi-body dynamics model of a vehicle with flexible wheelsets using finite element methods; simulations are performed to investigate the vibration characteristics of wheel polygonization. The paper measures the acceleration of axle boxes to monitor and detect wheel polygonization, adopting the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the signal into
several intrinsic mode functions (IMFs). The improved Hilbert-Huang transform (HHT) is performed, and the time-frequency spectrum is plotted to visually indicate the characteristic frequency of polygonization. Next, the Kullback-Leibler divergency is used to select the effective IMF; the mean frequency of the selected component is obtained to detect wheel polygonization and the corresponding order. Finally, simulations and field tests are performed to verify the effectiveness, accuracy, and robustness of the proposed method in a complex vibration environment.

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Published
2019-08-27
Section
Articles