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 Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

2020 ◽  
Vol 10 (8) ◽  
pp. 2786 ◽  
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Seyed Saeid Rahimian Koloor ◽  
Michal Petrů
Keyword(s):  
System Identification ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Practical Applications ◽  
Damage Scenarios ◽  
Advantages And Disadvantages ◽  
Civil Engineering Structures ◽  
Subspace System Identification

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.

scholarly journals ESTIMATION OF STRUCTURAL DYNAMICS OF A MODEL BOOM OF DRAGLINE DRE-23 BY OUTPUT-ONLY SYSTEM IDENTIFICATION METHODS

2011 ◽  
Author(s):  
Zakir Faruquee ◽  
Hal Gurgenci
Keyword(s):  
System Identification ◽  
Modal Analysis ◽  
Damping Ratio ◽  
Canonical Variate ◽  
Identification Methods ◽  
Analysis Methods ◽  
Electrodynamic Shaker ◽  
Simulated Field ◽  
Output Only ◽  
Frequency Domain Decomposition

Two output -only system identification methods namely Canonical Variate Analysis (CVA) and Frequency Domain Decomposition (FDD) were used to estimate the dynamics (Mode shape, natural frequency and damping ratio) of the model boom of the dragline DRE 23. The boom was excited separately with an impulse hammer and with an electrodynamic shaker with chirp, random and simulated field excitations. In all cases, the excitations as well as the responses of the model boom were measured. The dynamics were obtained from the response measurements using Output-Only methods as well as from both the excitations and responses using conventional modal analysis methods. In all cases, the estimations of the dynamics by Output-Only methods were comparable if not better than those estimates obtained by the convention modal analysis methods.

Input Excitation Analysis for Black-Box Quadrotor Model System Identification

2020 ◽  
Author(s):  
John Angarita ◽  
Daniel Doyle ◽  
Gustavo Gargioni ◽  
Jonathan Black
Keyword(s):  
System Identification ◽  
Dynamic Models ◽  
Moving Average ◽  
Black Box ◽  
Autoregressive Moving Average ◽  
Nonlinear Responses ◽  
Box Models ◽  
Armax Model ◽  
Quadrotor System ◽  
Error Method

Abstract System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.

System Identification of Small Loudspeakers Using ARMA Model

2010 ◽  
Author(s):  
Jaewon Choi ◽  
Mohsen Nakhaeinejad ◽  
Michael D. Bryant
Keyword(s):  
System Identification ◽  
Electrical Impedance ◽  
Moving Average ◽  
Arma Model ◽  
Data Driven ◽  
Autoregressive Moving Average ◽  
Autoregressive Moving Average Model ◽  
Driven System ◽  
Moving Average Model ◽  
Arma Modeling

This study illustrates a data driven system identification method for loudspeaker model estimation using the knowledge of the underlying physics of loudspeakers. In this study, diaphragm displacement is analyzed to estimate the model structure and parameters based on impulse response equivalent sampling and autoregressive moving average model. The estimated loudspeaker models are compared in the frequency response function plot. It is shown that the autoregressive moving average (ARMA) based loudspeaker models are comparable to the model estimated by the conventional method based on electrical impedance. Also ARMA modeling strategies with and without knowledge of the physics-based model are compared. Some issues related to ARMA modeling are addressed.

scholarly journals Highly efficient maximum-likelihood identification methods for bilinear systems with colored noises

2021 ◽  
Author(s):  
Meihang Li ◽  
Ximei Liu ◽  
Yamin Fan
Keyword(s):  
Maximum Likelihood ◽  
Computational Efficiency ◽  
Moving Average ◽  
Bilinear Systems ◽  
Autoregressive Moving Average ◽  
State Variables ◽  
Likelihood Principle ◽  
Identification Methods ◽  
Highly Efficient ◽  
Gradient Based

Abstract As a special class of nonlinear systems, bilinear systems can naturally describe many industrial production process. This paper mainly discussed the highly efficient iterative identification methods for bilinear systems with autoregressive moving average noise. Firstly, the input-output representation of the bilinear systems is derived through eliminating the unknown state variables in the model. Then based on the maximum-likelihood principle and the negative gradient search principle, a maximum-likelihood gradient-based iterative (ML-GI) algorithm is proposed to identify the parameters of the bilinear systems with colored noises. For further improving the computational efficiency, the original identification model is divided into three sub-identification models with smaller dimensions and fewer parameters, and a hierarchical maximum-likelihood gradient-based iterative (H-ML-GI) algorithm is derived by using the hierarchical identification principle. A gradient-based iterative (GI) algorithm is given for comparison. Finally, the algorithms are verified by a simulation example. The simulation results show that the proposed algorithms are effective for identifying bilinear systems with colored noises and the H-ML-GI algorithm has a higher computational efficiency and a faster convergence rate than the ML-GI algorithm and the GI algorithm.

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