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Electro-Mechanical Impedance-Based Wireless Structural Health Monitoring Using PCA-Data Compression and k-means Clustering Algorithms

Smart Infra-Structure Technology Center (SISTeC) Korea Advanced Institute of Science and Technology, Daejeon, 305-701, Republic of Korea

Jong-Jae Lee

Department of Civil and Environmental Engineering, Sejong University, Seoul, 143-747, South Korea, jongjae{at}sejong.ac.kr

Chung-Bang Yun

Smart Infra-Structure Technology Center (SISTeC) Korea Advanced Institute of Science and Technology, Daejeon, 305-701, Republic of Korea

Daniel J. Inman

Center for Intelligent Material Systems and Structures (CIMSS) Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA

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.

Key Words: electromechanical impedance • wireless • structural health monitoring • on-board active sensor system • self-sensing macro-fiber composite patch • principal component analysis • k-means clustering.

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