Identification of a railway wheelset system parameters

Wheelset is the basic component of a railway vehicle system. The wheel set has two wheels rigidly connected by an axle. There are mainly two non-constant values related to the wheelset that need to be estimated. They are the wheelset’s conicity and creep coefficients. Conicity (X) is a term related...

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主要作者: Nazari, Ain
格式: Thesis
出版: 2010
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spelling my-utm-ep.267802017-08-21T01:28:28Z Identification of a railway wheelset system parameters 2010 Nazari, Ain TK Electrical engineering. Electronics Nuclear engineering Wheelset is the basic component of a railway vehicle system. The wheel set has two wheels rigidly connected by an axle. There are mainly two non-constant values related to the wheelset that need to be estimated. They are the wheelset’s conicity and creep coefficients. Conicity (X) is a term related to the coning of the wheel tread and its value depends on the track and wheel profiles. For wheelset with profiled wheels, the conicity values is non-linear and depends on the relationship between the wheelset’s rolling radius difference and its lateral displacement Meanwhile, the creep coefficients (fit and fn) are related to the relative velocities of the wheel and the rail. This creep coefficient is non-linearly dependent on the normal force between the wheel and rail, and the value of this force is changing especially during curving. The values of the conicity and creep coefficients as well can be changed by other factors such as surface contamination, varying surface condition and railhead shapes. This project outlines the parameter estimator that can effectively estimate and track the time-varying conicity and creep coefficients. The performances of the Recursive Least Squares (RLS) and Recursive Instrumental Variable (RIV) used to estimate the conicity and creep coefficients values are compared. Via simulations, it is shown that the conicity and creep coefficients estimation using RLS algorithm takes longer time to converge to the true values and at the same time contains large amount of bias and oscillation. The RIV method overcome RLS problem in term of convergence and bias but the overshoot produced by this method is larger than RLS method. 2010 Thesis http://eprints.utm.my/id/eprint/26780/ http://libraryopac.utm.my/client/en_AU/main/search/results?qu=Identification+of+a+railway+wheelset+system+parameters&te= masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
topic TK Electrical engineering
Electronics Nuclear engineering
spellingShingle TK Electrical engineering
Electronics Nuclear engineering
Nazari, Ain
Identification of a railway wheelset system parameters
description Wheelset is the basic component of a railway vehicle system. The wheel set has two wheels rigidly connected by an axle. There are mainly two non-constant values related to the wheelset that need to be estimated. They are the wheelset’s conicity and creep coefficients. Conicity (X) is a term related to the coning of the wheel tread and its value depends on the track and wheel profiles. For wheelset with profiled wheels, the conicity values is non-linear and depends on the relationship between the wheelset’s rolling radius difference and its lateral displacement Meanwhile, the creep coefficients (fit and fn) are related to the relative velocities of the wheel and the rail. This creep coefficient is non-linearly dependent on the normal force between the wheel and rail, and the value of this force is changing especially during curving. The values of the conicity and creep coefficients as well can be changed by other factors such as surface contamination, varying surface condition and railhead shapes. This project outlines the parameter estimator that can effectively estimate and track the time-varying conicity and creep coefficients. The performances of the Recursive Least Squares (RLS) and Recursive Instrumental Variable (RIV) used to estimate the conicity and creep coefficients values are compared. Via simulations, it is shown that the conicity and creep coefficients estimation using RLS algorithm takes longer time to converge to the true values and at the same time contains large amount of bias and oscillation. The RIV method overcome RLS problem in term of convergence and bias but the overshoot produced by this method is larger than RLS method.
format Thesis
qualification_level Master's degree
author Nazari, Ain
author_facet Nazari, Ain
author_sort Nazari, Ain
title Identification of a railway wheelset system parameters
title_short Identification of a railway wheelset system parameters
title_full Identification of a railway wheelset system parameters
title_fullStr Identification of a railway wheelset system parameters
title_full_unstemmed Identification of a railway wheelset system parameters
title_sort identification of a railway wheelset system parameters
granting_institution Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department Faculty of Electrical Engineering
publishDate 2010
_version_ 1747815509080932352