Time-Frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition
Publication: Journal of Engineering Mechanics
Volume 142, Issue 1
Abstract
Currently, empirical mode decomposition (EMD) has become a popular data-driven time-frequency analysis method for nonstationary and nonlinear data. However, it is still limited to univariate data due to the number and/or scale misalignment for multivariate data. A newly developed multivariate EMD (MEMD) scheme decomposes multivariate data simultaneously and thus leads to mode alignment and minimizes mode mixing. In addition, an improved amplitude and frequency modulation (AM-FM) decomposition algorithm presented here provides an estimation of a more meaningful instantaneous amplitude and frequency than the widely used Hilbert transform (HT). Both of these facilitate development of a time-frequency analysis framework for multivariate nonstationary and nonlinear data analysis. In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectra and cospectra are proposed to characterize a multivariate nonstationary process. The scalogram and instantaneous frequency spectra capture spectral evolution of each component while the coscalogram and instantaneous frequency cospectra reveal embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectra and cospectra provide a more detailed portrait of multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is demonstrated by numerical examples of a thunderstorm downburst and an earthquake ground motion. Also, the results from the MEMD-based approach are compared with those based on a continuous wavelet transform, which further reinforces the effectiveness of the proposed framework.
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Acknowledgments
The support of the National 1000 Young Talents Program (China), the National Basic Research Program of China (No. 2013CB036300), and the Applied Basic Research Program of Sichuan Province (No. 2015JY0060) are greatly acknowledged.
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Published In
Journal of Engineering Mechanics
Volume 142 • Issue 1 • January 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Sep 15, 2014
Accepted: May 11, 2015
Published online: Jun 19, 2015
Discussion open until: Nov 19, 2015
Published in print: Jan 1, 2016
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