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Sensors 2015, 15(3), 6497-6519; doi:10.3390/s150306497

Sparse Component Analysis Using Time-Frequency Representations for Operational Modal Analysis

Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei 230027, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 27 November 2014 / Revised: 20 February 2015 / Accepted: 27 February 2015 / Published: 17 March 2015
(This article belongs to the Section Physical Sensors)

Abstract

Sparse component analysis (SCA) has been widely used for blind source separation(BSS) for many years. Recently, SCA has been applied to operational modal analysis (OMA), which is also known as output-only modal identification. This paper considers the sparsity of sources’ time-frequency (TF) representation and proposes a new TF-domain SCA under the OMA framework. First, the measurements from the sensors are transformed to the TF domain to get a sparse representation. Then, single-source-points (SSPs) are detected to better reveal the hyperlines which correspond to the columns of the mixing matrix. The K-hyperline clustering algorithm is used to identify the direction vectors of the hyperlines and then the mixing matrix is calculated. Finally, basis pursuit de-noising technique is used to recover the modal responses, from which the modal parameters are computed. The proposed method is valid even if the number of active modes exceed the number of sensors. Numerical simulation and experimental verification demonstrate the good performance of the proposed method. View Full-Text
Keywords: sparse component analysis; blind source separation; operational modal analysis; single source points sparse component analysis; blind source separation; operational modal analysis; single source points
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Qin, S.; Guo, J.; Zhu, C. Sparse Component Analysis Using Time-Frequency Representations for Operational Modal Analysis. Sensors 2015, 15, 6497-6519.

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