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Archive of Applied Mechanics

, Volume 81, Issue 11, pp 1541–1554 | Cite as

Application of inverse linear parametric models in the identification of rail track irregularities

  • Piotr CzopEmail author
  • Krzysztof Mendrok
  • Tadeusz Uhl
Original

Abstract

Requirements for current trains to be increasingly available have created the need to develop systems that can predict the quality of both trains and infrastructure components. The paper presents a new approach to the detection of rail truck irregularities, based on the measurements of bearing box acceleration during the operation of rail vehicles. The proposed procedure is based on an inverse problem solution, estimating track irregularities from measured acceleration of the applied model of vehicle dynamics. The simulation study of the proposed method, as well as its implementation, is presented. The method has been successfully applied for the identification of rail irregularities on a typical Polish railroad and vehicle.

Keywords

Rail irregularities Linear model Inverse model Data-driven model 

Abbreviations

AIC

Akaike’s information criterion

FPE

Akaike’s final prediction error

LTI

Linear and time-invariant model/system

ARX

AutoRegressive with eXogeneous input

ARMAX

AutoRegressive moving average with eXogeneous input

BJ

Box-Jenkins

PEM

Prediction error method

OE

Output error

FRF

Frequency response function

SISO

Single-input single-output

List of symbols

i

Discrete time

A, B, C, D, E, F

Polynomials used for the representation of the transfer function

nA, nB, nC, nE, nF

Order of polynomials used for the representation of the transfer function

z

Operator of the Z transformation

e

Disturbance variables in the model

u

Input variables in the model

u0

Inverse input

y

Output variables in the model

G(z−1)

Transfer function

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of Robotics and MechatronicsAGH University of Science and TechnologyKrakowPoland

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