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

Sensors

Shaoqian Qin²  Jie Guo¹  Changan Zhu² 

[1] Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei 230027, China

关键词: sparse component analysis;    blind source separation;    operational modal analysis;    single source points;   

DOI  :  10.3390/s150306497

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.