Computing and Library Services - delivering an inspiring information environment

An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm

Liu, Xiaoyuan, Alfi, Stefano and Bruni, Stefano (2016) An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm. In: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics. IAVSD 2015 . Taylor & Francis, Graz, Austria, p. 861. ISBN 978-1-4987-7702-5

[img] PDF
Restricted to Registered users only

Download (1MB)


This paper presents a model-based strategy 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. RLS is able to identify the unknown
parameters from a noisy input-output system by memorizing the 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
suspensions 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. The test data from
an E464 locomotive are also employed to validate the feasibility of this strategy for the real
situation. Results of the parameter identification performed indicate that estimated suspension
parameters are consistent or approximate with the values for reference, thereby supporting the
application of this fault diagnosis technique to the future condition monitoring system of the
rail vehicle suspension

Item Type: Book Chapter
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: 14 Mar 2017 11:46
Last Modified: 28 Aug 2021 16:12


Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©