A data-driven method for estimating wheel flat length. Issue 9 (1st September 2020)

• Record Type: Journal Article
• Authors: Ye, Yunguang; Shi, Dachuan; Krause, Philipp; Hecht, Markus
• Is Part Of: Vehicle system dynamics. Volume 58:Issue 9(2020)
• Page Start: 1329
• Page End: 1347
• Publication Date: 2020-09-01
• Subjects: Wheel flat -- flat length -- surrogate model -- particle swarm optimisation -- data-driven -- condition monitoring; Motor vehicles -- Dynamics -- Periodicals; Electronic journals; 629.231
• DOI: 10.1080/00423114.2019.1620956
• Languages: English
• ISSNs: 0042-3114

Abstract

ABSTRACT: Wheel flat is one of the most common defects occurring to railway wheels. The relevant standards have specified the operational limits for wheel flats in terms of the length. Therefore, the information on the flat length is required for maintenance decision. In this sense, this paper proposes a data-driven method not only for detecting wheel-flats but also for estimating the flat length, which can be implemented for onboard condition monitoring. Firstly, A multibody dynamics model of a Y25-tank-wagon with a wheel flat of a variable length is established to generate the axlebox acceleration data at variable vehicle speeds. Then, based on the selected simulation data points, a Kriging surrogate model (KSM) is constructed to model the axlebox acceleration response to different lengths of wheel flats and different vehicle speeds. Finally, a particle swarm optimisation (PSO) based algorithm is applied to calculate the exact wheel-flat length by feeding the measured vehicle speed and the acceleration signal into the KSM model. The proposed method is validated by a field test, for which a wheel flat with a length of 20 mm was artificially produced. Simulation and experimental results have demonstrated that this method can estimate the length of wheel flats.