A data-driven method for estimating wheel flat length

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.

KEYWORDS:

• Wheel flat
• flat length
• surrogate model
• particle swarm optimisation
• data-driven
• condition monitoring

Acknowledgment

The experiment data used in this paper is supported by the Energieautarke Sensorsysteme zur Zustandsüberwachung von Güterwagen (ESZüG) project of Germany, and the research fund is supported by China Scholarship Council. The authors would like to thank Mr. Xiangyu Qu from Beijing Jiaotong University for his suggestions concerning the elastic track modeld in this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by China Scholarship Council [grant number 201707000113].