Estimation of railway vehicle suspension parameters for condition monitoring

P li; R Goodall; Paul Weston; Chung Ling; Colin Goodman; Clive Roberts

doi:10.1016/j.conengprac.2006.02.021

Estimation of railway vehicle suspension parameters for condition monitoring

P li, R Goodall, Paul Weston, Chung Ling, Colin Goodman, Clive Roberts

Electronic, Electrical and Systems Engineering

Research output: Contribution to journal › Article › peer-review

146 Citations (Scopus)

Abstract

This paper investigates the problem of parameter estimation for railway vehicle suspensions so as to provide information to support condition-based (instead of calendar-based) maintenance. A simplified plan view railway vehicle dynamical model is derived and a newly developed Rao-Blackwellized particle filter (RBPF) based method is used for parameter estimation. Computer simulations are carried out to assess and compare the performance of parameter estimation with different sensor configurations as well as the robustness with respect to the uncertainty in the statistics of the random track inputs. The method is then verified practically using real test data from a Coradia Class 175 railway vehicle with only bogie and body mounted sensors, and some preliminary results are presented.

Original language	English
Pages (from-to)	43-55
Number of pages	13
Journal	Control Engineering Practice
Volume	15
Issue number	1
Early online date	17 Apr 2006
DOIs	https://doi.org/10.1016/j.conengprac.2006.02.021
Publication status	Published - 1 Jan 2007

Keywords

Condition monitoring
Extended Kalman filter
Parameter estimation
Railway vehicle dynamical modelling
Rao-Blackwellized particle filter

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

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https://www.sciencedirect.com/science/article/pii/S0967066106000463?via%3DihubLicence: None: All rights reserved

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title = "Estimation of railway vehicle suspension parameters for condition monitoring",

abstract = "This paper investigates the problem of parameter estimation for railway vehicle suspensions so as to provide information to support condition-based (instead of calendar-based) maintenance. A simplified plan view railway vehicle dynamical model is derived and a newly developed Rao-Blackwellized particle filter (RBPF) based method is used for parameter estimation. Computer simulations are carried out to assess and compare the performance of parameter estimation with different sensor configurations as well as the robustness with respect to the uncertainty in the statistics of the random track inputs. The method is then verified practically using real test data from a Coradia Class 175 railway vehicle with only bogie and body mounted sensors, and some preliminary results are presented.",

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AB - This paper investigates the problem of parameter estimation for railway vehicle suspensions so as to provide information to support condition-based (instead of calendar-based) maintenance. A simplified plan view railway vehicle dynamical model is derived and a newly developed Rao-Blackwellized particle filter (RBPF) based method is used for parameter estimation. Computer simulations are carried out to assess and compare the performance of parameter estimation with different sensor configurations as well as the robustness with respect to the uncertainty in the statistics of the random track inputs. The method is then verified practically using real test data from a Coradia Class 175 railway vehicle with only bogie and body mounted sensors, and some preliminary results are presented.

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Estimation of railway vehicle suspension parameters for condition monitoring

Abstract

Keywords

ASJC Scopus subject areas

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