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Condition monitoring of rail vehicle suspension based on recursive least-square algorithm

Liu, Xiaoyuan, Alfi, Stefano and Bruni, Stefano (2014) Condition monitoring of rail vehicle suspension based on recursive least-square algorithm. In: Proceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies : VSDIA 2014. Budapest University of Technology and Economics, Budapest, pp. 47-54. ISBN 9789633131862

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Abstract

This paper presents a model-based method for condition monitoring of suspensions in a railway bogie. This approach is based on recursive least-square (RLS) algorithm focusing on the ‘Input-output’ model instead of the ‘State Space’ model. RLS estimates the unknown parameters from an input-output system by memorizing its correlation properties. The identification of the suspension parameter is achieved by establishing the relationship between the excitation and response of a bogie. A fault detection method for vertical primary dampers of one bogie is illustrated as an example of this scheme. Numerical simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilized as ‘virtual measurements’, considering a trailed car of Italian ETR500 high-speed train. Results of the parameter identification performed on the virtual measurements indicate that estimated suspension parameters are consistent with the values adopted in the numerical simulations, thereby supporting the application of this technique for the fault detection and isolation to real cases.

Item Type: Book Chapter
Additional Information: 14th mini conference on vehicle system dynamics, identification and anomalies : VSDIA 2014, held at the Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics, Hungary, Budapest, 10-12 November, 2014 / ed. by I. Zobory. – Budapest, 2014
Subjects: T Technology > TF Railroad engineering and operation
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Xiaoyuan Liu
Date Deposited: 09 Mar 2017 15:25
Last Modified: 09 Mar 2017 15:26
URI: http://eprints.hud.ac.uk/id/eprint/31448

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