A dynamic detection method for polygonal wear of railway wheel based on parametric power spectral estimation

Abstract

Severe wheel polygon will cause the strong repeated wheel-rail excitation, accelerating the failure of vehicle key components. Nevertheless, the traditional spectral estimation methods are not ideal for dealing with these wheel fault signals, making the characteristic frequencies caused by wheel polygon hardly detected. This paper proposes a dynamic detection method for wheel polygon based on parametric power spectral estimation for the first time. Firstly, according to the dynamic characteristics of the wheel polygon, the harmonic frequency recovery model is established. Secondly, the order of the harmonic frequency recovery model is determined based on singular value decomposition and normalised error analysis. Then, the total least square method is used to calculate the parameters of the harmonic recovery model. Finally, the power spectrum of the fault signal is evaluated according to the Cadzow estimation theory. The simulation signal and the measured axle-box vertical vibration acceleration signal of a metro vehicle are taken as case studies to verify the feasibility and effectiveness of the proposed method thoroughly. The results show the proposed method can avoid the inherent defects of traditional detection and analysis methods, weaken the influence of background noise, and is highly advantageous in detecting the initial stage of wheel polygonal wear.

KEYWORDS:

• Rail vehicle
• wheel polygonal wear
• parametric power spectral estimation
• detection
• axle box vibration acceleration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (grant number 51805373). And thanks the China Scholarship Council for supporting Qiushi Wang to study at school of Mechanical and Aerospace Engineering in Nanyang Technological University, Singapore, the support number is 202106260138.