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Review of adhesion estimation approaches for rail vehicles

journal contribution
posted on 19.11.2019, 00:00 by Sundar Shrestha, Qing Wu, Maksym Spiryagin
The estimation of adhesion conditions between wheels and rails during railway operations is an important task as it helps to characterise the braking and traction control system. Since the adhesion condition is influenced by many factors, its estimation process is complex. This paper reviews the existing approaches to estimate adhesion conditions. These approaches are model-based prediction, inverse dynamic modelling, Kalman filter method, artificial neural network method and particle swarm optimisation method. The classification, methodologies, theories and applications of these approaches are included in this paper. The advantages and limitations of these methods are analysed to provide an application recommendation for adhesion estimation. This review has concluded that all estimation approaches undergo a linearisation stage where error cannot be avoided. The trade-off between accuracy and analysis time must be considered. This review also discusses how to improve existing approaches to achieve a more precise estimation of adhesion conditions.

Funding

Category 4 - CRC Research Income

History

Volume

7

Issue

2

Start Page

79

End Page

102

Number of Pages

24

eISSN

2324-8386

ISSN

2324-8378

Publisher

Informa UK Limited

Language

en

Peer Reviewed

Yes

Open Access

No

Acceptance Date

15/08/2018

Author Research Institute

Centre for Railway Engineering

Era Eligible

Yes

Journal

International Journal of Rail Transportation

Exports