An on-board detection framework for polygon wear of railway wheel based on vibration acceleration of axle-box. (15th May 2021)

• Record Type: Journal Article
• Authors: Sun, Qi; Chen, Chunjun; Kemp, Andrew H.; Brooks, Peter
• Is Part Of: Mechanical systems and signal processing. Volume 153(2021)
• Publication Date: 2021-05-15
• Subjects: Railway wheel polygonization -- Rotating machine fault diagnosis -- Angle domain synchronous averaging technique -- Axle-box vibration acceleration; ABVA axle-box vibration acceleration -- ADSAT angle domain synchronous averaging technique -- CRH1/CRH2/CRH3/CRH5 China Railway High-speed 1, 2, 3, 5 -- DAQ data acquisition -- DTFT Discrete-time Fourier transform -- FE-SEA the finite element method and the statistic energy analysis -- FSWT frequency slice wavelet transform -- FV feature vector -- HHT Hilbert-Huang transform -- IVW the inner-circle vibration acceleration vector of the wheel -- OOR out-of-roundness -- PSD power spectral density -- PWRW polygon wear of railway wheel -- RMS root mean square -- SNR signal-noise-ratio; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107540
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: It exploits the vertical axle-box vibration acceleration signal that it is helpful to simplify the hardware of monitoring system of high-speed train. The proposed angular synchronous average technique perfectly enhances the fault-related signal of wheel polygon wear by mitigating the asynchronous coherent and random background noise. The proposed method can detect the order and the rough degree of railway wheel polygonization fault in real-time. Abstract: The polygon wear of railway wheel (PWRW) is a wear fault that is ubiquitous in railway vehicles. PWRW can induce a strong periodic excitation to both vehicle and track, which not only decreases passenger comfort but also is detrimental to the operational reliability and safety. Both the degree and the order of PWRW are important parameters used to quantify the fault. Because the fault-related components distribute at a wide range in the frequency domain, it is easy to alias with some radiated vibrations from vehicle and track components, which makes the on-board detection for both parameters of PWRW very difficult. To address the practical engineering problem, this paper proposes a detection framework based on the angle domain synchronous averaging technique (ADSAT). The detection method employs the vertical axle-box vibration acceleration (ABVA), which is easy to obtain and can also be used to monitor the conditions of axle-box bearings. The paper compares the proposed and traditional methods. The results reveal that the proposed method not only achieves the order detection which the traditional method cannot, but also mitigates the influence of background noise. The feasibility and effectiveness of the proposed method to improve the detection accuracy of PWRW is demonstrated through simulation and real field investigations.