An on-board detection framework for polygon wear of railway wheel based on vibration acceleration of axle-box

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The study focuses on the detection of polygon wear of railway wheel (PWRW), a common wear fault in railway vehicles that can cause strong periodic excitation, decrease passenger comfort, and impact operational reliability and safety. A detection framework based on the angle domain synchronous averaging technique (ADSAT) using vertical axle-box vibration acceleration (ABVA) is proposed to improve the accuracy of PWRW detection. Results show that the proposed method can achieve order detection that traditional methods cannot, while also reducing the influence of background noise.