scholarly journals Output-Only Identification of System Parameters from Noisy Measurements by Multiwavelet Denoising

2014 ◽  
Vol 6 ◽  
pp. 218328 ◽  
Author(s):  
O. Al-Gahtani ◽  
M. El-Gebeily ◽  
Y. Khulief
Keyword(s):  
Wavelet Transforms ◽  
Model Identification ◽  
Subspace Identification ◽  
Measuring Devices ◽  
Noisy Output ◽  
Output Only ◽  
Modal Values ◽  
Noisy Measurements ◽  
Stochastic Identification ◽  
Wavelet Methods

In this paper we estimate the parameters of a multidimensional system from a record of noisy output measurements by using a multiwavelet denoising technique. In this output-only identification scheme, we extend wavelet denoising methods to the multiwavelet case. After the noise has been removed from the output records by wavelet methods, either full model identification or deterministic subspace identification can be performed. In the former case, full information on the system such as modal values and shapes becomes available by postprocessing. In the latter case, the observable modal values of the system as well as modal shapes at the sensor locations can be extracted from the identified parameters. Additionally, we discuss the requirements on the measuring devices to be compatible with wavelet transforms of a particular type. The validity and merit of the developed scheme are illustrated by examples of numerically simulated and experimentally measured signals, including comparisons with stochastic identification methods.

Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice1

2001 ◽  
Vol 123 (4) ◽  
pp. 668-676 ◽  
Author(s):  
Miche`le Basseville ◽  
Albert Benveniste ◽  
Maurice Goursat ◽  
Luc Hermans ◽  
Laurent Mevel ◽  
...  
Keyword(s):  
Structural Model ◽  
Model Identification ◽  
Structural Identification ◽  
Measured Data ◽  
Operating Conditions ◽  
Industrial Testing ◽  
Identification Methods ◽  
Multiple Sensor ◽  
Output Only ◽  
Stochastic Identification

We address the problem of structural model identification during normal operating conditions and thus with uncontrolled, unmeasured, and nonstationary excitation. We advocate the use of output-only and covariance-driven subspace-based stochastic identification methods. We explain how to handle nonsimultaneously measured data from multiple sensor setups, and how robustness with respect to nonstationary excitation can be achieved. Experimental results obtained for three real application examples are shown.

Stochastic System Identification for Operational Modal Analysis: A Review

2001 ◽  
Vol 123 (4) ◽  
pp. 659-667 ◽  
Author(s):  
Bart Peeters ◽  
Guido De Roeck
Keyword(s):  
System Identification ◽  
Stochastic System ◽  
Instrumental Variable ◽  
Subspace Identification ◽  
Response Functions ◽  
Impulse Responses ◽  
Identification Methods ◽  
Operational Conditions ◽  
Stochastic Subspace Identification ◽  
Output Only

This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

scholarly journals USING WAVELETS IN ECONOMICS. AN APPLICATION ON THE ANALYSIS OF WAGE-PRICE RELATION

2017 ◽  
Vol 2 (1) ◽  
pp. 32-42
Author(s):  
Vasile-Aurel Caus ◽  
Daniel Badulescu ◽  
Mircea Cristian Gherman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Fourier Transforms ◽  
Nominal Wage ◽  
Economic Issues ◽  
Wavelet Methods ◽  
Theoretical Considerations ◽  
Analysis Of Time Series

In the last decades, more and more approaches of economic issues have used mathematical tools, and among the most recent ones, spectral and wavelet methods are to be distinguished. If in the case of spectral analysis the approaches and results are sufficiently clear, while the use of wavelet decomposition, especially in the analysis of time series, is not fully valorized. The purpose of this paper is to emphasize how these methods are useful for time series analysis. After theoretical considerations on Fourier transforms versus wavelet transforms, we have presented the methodology we have used and the results obtained by using wavelets in the analysis of wage-price relation, based on some empirical data. The data we have used is concerning the Romanian economy - the inflation and the average nominal wage denominated in US dollars, between January 1991 and May 2016, highlighting that the relation between nominal salary and prices can be revealed more accurately by use of wavelets

Recursive Subspace Identification Algorithm using the Propagator Based Method

2017 ◽  
Vol 6 (1) ◽  
pp. 172 ◽  
Author(s):  
Irma Wani Jamaludin Wani Jamaludin ◽  
Norhaliza Abdul Wahab
Keyword(s):  
State Space ◽  
Model Identification ◽  
Industrial Applications ◽  
Dynamic State ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Online Application ◽  
Subspace Algorithms ◽  
State Space System ◽  
Value Decomposition

<p>Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the classical SMI algorithms are not suitable for online application. Hence, it is essential to discover the alternative algorithms in order to apply the concept of subspace identification recursively. In this paper, the recursive subspace identification algorithm based on the propagator method which avoids the SVD computation is proposed. The output from Numerical Subspace State Space System Identification (N4SID) and Multivariable Output Error State Space (MOESP) methods are also included in this paper.</p>

Quantitative finance for agricultural commodities: discussion and extension

2016 ◽  
Vol 76 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Gabriel J. Power
Keyword(s):  
Time Series ◽  
Wavelet Transforms ◽  
Time Horizon ◽  
Fractional Integration ◽  
Agricultural Commodities ◽  
Futures Trading ◽  
Content Type ◽  
Quantitative Finance ◽  
Time Series Econometrics ◽  
Wavelet Methods

Purpose – The purpose of this paper is to review three papers in this issue and contribute new results on commodity futures prices and volume using wavelet analysis. Design/methodology/approach – The paper uses time series econometrics including variance ratio tests, fractional integration estimators, and wavelet transforms. Findings – The role of time horizon is emphasized in the discussion of the three papers, and wavelet methods are shown to be a useful tool to better understand time horizon-specific risk. Moreover, changes in the time horizon of futures trading are documented and discussed. Originality/value – In addition to discussing three papers on quantitative finance for agricultural commodities, this paper also looks at how the analysis and management of short-term and long-term risk may differ. To this end, wavelet transform-based time series methods are reviewed and applied.

scholarly journals Yonjung High-Speed Railway Bridge Assessment Using Output-Only Structural Health Monitoring Measurements under Train Speed Changing

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu ◽  
Mohamed A. Sayed
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Prestressed Concrete ◽  
High Speed ◽  
Model Identification ◽  
Steel Girders ◽  
Structural Health ◽  
High Speed Trains ◽  
Output Only ◽  
Bridge Assessment

Yonjung Bridge is a hybrid multispan bridge that is designed to transport high-speed trains (HEMU-430X) with maximum operating speed of 430 km/h. The bridge consists of simply supported prestressed concrete (PSC) and composite steel girders to carry double railway tracks. The structural health monitoring system (SHM) is designed and installed to investigate and assess the performance of the bridge in terms of acceleration and deformation measurements under different speeds of the passing train. The SHM measurements are investigated in both time and frequency domains; in addition, several identification models are examined to assess the performance of the bridge. The drawn conclusions show that the maximum deflection and acceleration of the bridge are within the design limits that are specified by the Korean and European codes. The parameters evaluation of the model identification depicts the quasistatic and dynamic deformations of PSC and steel girders to be different and less correlated when higher speeds of the passing trains are considered. Finally, the variation of the frequency content of the dynamic deformations of the girders is negligible when high speeds are considered.

Experimental validation of the Kalman-type filters for online and real-time state and input estimation

2015 ◽  
Vol 23 (15) ◽  
pp. 2494-2519 ◽  
Author(s):  
Saeed Eftekhar Azam ◽  
Eleni Chatzi ◽  
Costas Papadimitriou ◽  
Andrew Smyth
Keyword(s):  
Kalman Filter ◽  
Physical Models ◽  
Mode Shapes ◽  
Small Scale ◽  
Simultaneous Estimation ◽  
Subspace Identification ◽  
Entire Body ◽  
Acceleration Time Histories ◽  
Time Histories ◽  
Output Only

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.

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