&lt;title&gt;Multiscale system identification and estimation&lt;/title&gt;

Le, Dzu K.

doi:10.1117/12.211423

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Farge et al [14] applied CHW ideas into the computation of two-dimensional turbulent flows, where the wavelet packet best-basis seems to distinguish the low-dimensional dynamically active part of the flow from the high-dimensional passive components, providing a hope of drastically reducing the number of degrees of freedom necessary to the computation. Le [15] did a practicable framework to represent the wavelets transform of linear dynamics delivering a formula for projecting linear time-invariant dynamics and stochastic processes on the wavelet spaces of a multiresolution analysis; applications of this formula to system and parameter identification for control and modeling of mechanical systems were demonstrated. Latest, Yao et al [16] used the STFT to estimate the linear frequency modulation signal parameters and the experimental results verified its validity and feasibility.…”

Section: Introductionmentioning

confidence: 99%

Rotor-System Log-Decrement Identification Using Short-Time Fourier-Transform Filter

Wang

Li-fang

et al. 2015

International Journal of Rotating Machinery

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With the increase of the centrifugal compressor capability, such as large scale LNG and CO2reinjection, the stability margin evaluation is crucial to assure the compressor work in the designed operating conditions in field. Improving the precision of parameter identification of stability is essential and necessary as well. Based on the time-varying characteristics of response vibration during the sine-swept process, a short-time Fourier transform (STFT) filter was introduced to increase the signal-noise ratio and improve the accuracy of the estimated stability parameters. A finite element model was established to simulate the sine-swept process, and the simulated vibration signals were used to study the filtering effect and demonstrate the feasibility to identify the stability parameters by using Multiple-Input and Multiple-Output system identification method that combines the prediction error method and instrumental variable method. Simulation results show that the identification method with STFT filter improves the estimated accuracy much well and makes the curves of frequency response function clearer. Experiment was carried out on a test rig as well, which indicates the identification method is feasible in stability identification, and the results of experiment indicate that STFT filter works very well.

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Section: Introductionmentioning

confidence: 99%

Rotor-System Log-Decrement Identification Using Short-Time Fourier-Transform Filter

Wang

Li-fang

et al. 2015

International Journal of Rotating Machinery

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“…Obviously such models may easily be related to the original system parameters that can provide a clear physical explanation. The identification of PDE models of continuous spatio-temporal systems has been studied by several authors ( Le 1995). However, very little has been done for the PDE model identification problem of multiscale systems directly from observations.…”

Section: Introductionmentioning

confidence: 99%

Identification of partial differential equation models for a class of multiscale spatio-temporal dynamical systems

Guo

Billings

Coca

2009

International Journal of Control

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In this paper, the identification of a class of multiscale spatio-temporal dynamical systems, which incorporate multiple spatial scales, from observations is studied. The proposed approach is a combination of Adams integration and an orthogonal least squares algorithm, in which the multiscale operators are expanded, using polynomials as basis functions, and the spatial derivatives are estimated by finite difference methods. The coefficients of the polynomials can vary with respect to the space domain to represent the feature of multiple scales involved in the system dynamics and are approximated using a B-spline wavelet multiresolution analysis (MRA). The resulting identified models of the spatio-temporal evolution form a system of partial differential equations with different spatial scales. Examples are provided to demonstrate the efficiency of the proposed method.

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“…That direction for control design was proposed by Le 21 and named the Multi-Scale Extended Kalman approach in this paper. It was inspired by the fact that, non-stationaty processes (e.g., the Fractional Brownian Motions) become stationary under proper wavelet transformations.…”

mentioning

confidence: 99%

Control of Thermo-Acoustics Instabilities: The Multi-Scale Extended Kalman Approach

DeLaat

Chang

2003

39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit

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<title>Multiscale system identification and estimation</title>

Cited by 3 publications

References 4 publications

Rotor-System Log-Decrement Identification Using Short-Time Fourier-Transform Filter

Rotor-System Log-Decrement Identification Using Short-Time Fourier-Transform Filter

Identification of partial differential equation models for a class of multiscale spatio-temporal dynamical systems

Control of Thermo-Acoustics Instabilities: The Multi-Scale Extended Kalman Approach

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