Quantitative detection of locomotive wheel polygonization under non-stationary conditions by adaptive chirp mode decomposition

Abstract

Wheel polygonal wear is a common and severe defect, which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive. Due to non-stationary running conditions (e.g., traction and braking) of the locomotive, the passing frequencies of a polygonal wheel will exhibit time-varying behaviors, which makes it too difficult to effectively detect the wheel defect. Moreover, most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels. To address these issues, this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition (ACMD) approach. Firstly, a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor. After the rotating frequency is obtained, signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear. Finally, the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes. Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.