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.

铁路车轮的多边形磨损（PWRW）是在铁路车辆中普遍存在的磨损故障。PWRW会对车辆和轨道产生强烈的周期性激励，这不仅会降低乘客的舒适度，而且还会损害运行的可靠性和安全性。PWRW的程度和阶数都是用于量化故障的重要参数。由于与故障相关的组件在频域中分布很广，因此很容易混叠来自车辆和轨道组件的辐射振动，这使得对PWRW的两个参数进行车载检测非常困难。针对实际工程问题，本文提出了一种基于角域同步平均技术（ADSAT）的检测框架。检测方法采用垂直轴箱振动加速度（ABVA），这很容易获得，也可以用于监视轴箱轴承的状况。本文比较了建议的方法和传统方法。结果表明，该方法不仅实现了传统方法无法实现的阶次检测，而且减轻了背景噪声的影响。通过仿真和实地研究证明了该方法提高压水堆探测精度的可行性和有效性。