Recent developments in damage detection based on system identification methods

1990 ◽  
Vol 2 (1) ◽  
pp. 1-10 ◽  
Author(s):  
P. Hajela ◽  
F. J. Soeiro
Keyword(s):  
System Identification ◽  
Damage Detection ◽  
Identification Methods ◽  
Recent Developments

scholarly journals EXPERIMENTAL IDENTIFICATION OF AN ELASTO-MECHANICAL MULTI-DEGREE-OF-FREEDOM-SYSTEM USING STOCHASTIC SIGNALS

2016 ◽  
Vol 7 ◽  
pp. 64 ◽  
Author(s):  
Simon Schleiter ◽  
Okyay Altay ◽  
Sven Klinkel
Keyword(s):  
System Identification ◽  
Civil Engineering ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Engineering Structures ◽  
Stochastic Signals ◽  
Identification Methods ◽  
Recent Developments ◽  
Rc Building ◽  
Frequency Domain Decomposition

The determination of dynamic parameters are the central points of the system identification of civil engineering structures under dynamic loading. This paper first gives a brief summary of the recent developments of the system identification methods in civil engineering and describes mathematical models, which enable the identification of the necessary parameters using only stochastic input signals. Relevant methods for this identification use Frequency Domain Decomposition (FDD), Autoregressive Moving Average Models (ARMA) and the Autoregressive Models with eXogenous input (ARX). In a first step an elasto-mechanical mdof-system is numerically modeled using FEM and afterwards tested numerically by above mentioned identification methods using stochastic signals. During the second campaign, dynamic measurements are conducted experimentally on a real 7-story RC-building with ambient signal input using sensors. The results are successfully for the relevant system identification methods.

scholarly journals Damage Location Index of Shear Structures Based on Changes in First Two Natural Frequencies

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hien HoThu ◽  
Akira Mita
Keyword(s):  
System Identification ◽  
Damage Detection ◽  
Natural Frequencies ◽  
Detection Method ◽  
Identification Methods ◽  
Damage Location ◽  
Shear Structures ◽  
Stiffness Reduction ◽  
Shear Building

A method of detecting the location of damage in shear structures by using only the changes in first two natural frequencies of the translational modes is proposed. This damage detection method can determine the damage location in a shear building by using a Damage Location Index (DLI) based on two natural frequencies for undamaged and damaged states. In this study, damage is assumed to be represented by the reduction in stiffness. This stiffness reduction results in a change in natural frequencies. The uncertainty associated with system identification methods for obtaining natural frequencies is also carefully considered. Some simulations and experiments on shear structures were conducted to verify the performance of the proposed method.

System Identification Methods for Improving Flutter Flight Test Techniques

2004 ◽  
Author(s):  
David Klyde ◽  
Chuck Harris ◽  
Peter M. Thompson ◽  
Edward N. Bachelder
Keyword(s):  
System Identification ◽  
Flight Test ◽  
Identification Methods

Numerical deconvolution using system identification methods

1988 ◽  
Vol 16 (1) ◽  
pp. 85-107 ◽  
Author(s):  
Sandor Vajda ◽  
Keith R. Godfrey ◽  
Peter Valko
Keyword(s):  
System Identification ◽  
Identification Methods ◽  
Numerical Deconvolution

scholarly journals Quantitative comparison of time-varying system identification methods to describe human joint impedance

2021 ◽  
Author(s):  
Mark van de Ruit ◽  
Winfred Mugge ◽  
Gaia Cavallo ◽  
John Lataire ◽  
Daniel Ludvig ◽  
...  
Keyword(s):  
System Identification ◽  
Quantitative Comparison ◽  
Time Varying ◽  
Identification Methods ◽  
Human Joint

scholarly journals Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Lan Wang ◽  
Yu Cheng ◽  
Jinglu Hu ◽  
Jinling Liang ◽  
Abdullah M. Dobaie
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Radial Basis Function Network ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Identification Procedure ◽  
Identification Methods ◽  
Nonlinear Parameter Identification ◽  
And Control ◽  
Function Network

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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