Electro-mechanical impedance-based wireless structural health monitoring using PCA-data compression and k-means clustering algorithms

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This article presents a practical method for an electro-mechanical impedance-based wireless structural health monitoring (SHM), which incorporates the principal component analysis (PCA)-based data compression and k-means clustering-based pattern recognition. An on-board active sensor system, which consists of a miniaturized impedance measuring chip (AD5933) and a self-sensing macro-fiber composite (MFC) patch, is utilized as a next-generation toolkit of the electromechanical impedance-based SHM system. The PCA algorithm is applied to the raw impedance data obtained from the MFC patch to enhance a local data analysis-capability of the on-board active sensor system, maintaining the essential vibration characteristics and eliminating the unwanted noises through the data compression. Then, the root-mean square-deviation (RMSD)-based damage detection result using the PCA-compressed impedances is compared with the result obtained from the raw impedance data without the PCA preprocessing. Furthermore, the k-means clustering-based unsupervised pattern recognition, employing only two principal components, is implemented. The effectiveness of the proposed methods for a practical use of the electromechanical impedance-based wireless SHM is verified through an experimental study consisting of inspecting loose bolts in a bolt-jointed aluminum structure.
Publisher
SAGE PUBLICATIONS LTD
Issue Date
2008-04
Language
English
Article Type
Article
Citation

JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, v.19, no.4, pp.509 - 520

ISSN
1045-389X
DOI
10.1177/1045389X07077400
URI
http://hdl.handle.net/10203/8445
Appears in Collection
CE-Journal Papers(저널논문)
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