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Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition

Parametric identification of time-varying systems from free vibration using intrinsic chirp... Time-varying systems are applied extensively in practical applications, and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems. To improve the identification accuracy for time-varying systems, this study puts forward a novel parameter identification approach in the time–frequency domain using intrinsic chirp component decomposition (ICCD). ICCD is a powerful tool for signal decomposition and parameter extraction, with good signal reconstruction capability in a high-noise environment. To maintain good identification effects for the time-varying system in a noisy environment, the proposed method introduces a redundant Fourier model for the non-stationary signal, including instantaneous frequency (IF) and instantaneous amplitude (IA). The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters, as well as an example of a multi-degree-of-freedom system. The effects of different levels of measured noise on the identified results are also discussed in detail. Numerical results show that the proposed method is very effective in tracking the smooth, periodical, and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.Graphic abstract[graphic not available: see fulltext] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Acta Mechanica Sinica" Springer Journals

Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition

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References (28)

Publisher
Springer Journals
Copyright
Copyright © The Chinese Society of Theoretical and Applied Mechanics and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Subject
Engineering; Theoretical and Applied Mechanics; Classical and Continuum Physics; Engineering Fluid Dynamics; Computational Intelligence
ISSN
0567-7718
eISSN
1614-3116
DOI
10.1007/s10409-019-00905-7
Publisher site
See Article on Publisher Site

Abstract

Time-varying systems are applied extensively in practical applications, and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems. To improve the identification accuracy for time-varying systems, this study puts forward a novel parameter identification approach in the time–frequency domain using intrinsic chirp component decomposition (ICCD). ICCD is a powerful tool for signal decomposition and parameter extraction, with good signal reconstruction capability in a high-noise environment. To maintain good identification effects for the time-varying system in a noisy environment, the proposed method introduces a redundant Fourier model for the non-stationary signal, including instantaneous frequency (IF) and instantaneous amplitude (IA). The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters, as well as an example of a multi-degree-of-freedom system. The effects of different levels of measured noise on the identified results are also discussed in detail. Numerical results show that the proposed method is very effective in tracking the smooth, periodical, and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.Graphic abstract[graphic not available: see fulltext]

Journal

"Acta Mechanica Sinica" – Springer Journals

Published: Feb 1, 2020

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