DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Seunghee | ko |
dc.contributor.author | Lee, Jong-Jae | ko |
dc.contributor.author | Yun, Chung Bang | ko |
dc.contributor.author | Inman, Daniel J. | ko |
dc.date.accessioned | 2009-02-10T06:38:58Z | - |
dc.date.available | 2009-02-10T06:38:58Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2008-04 | - |
dc.identifier.citation | JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, v.19, no.4, pp.509 - 520 | - |
dc.identifier.issn | 1045-389X | - |
dc.identifier.uri | http://hdl.handle.net/10203/8445 | - |
dc.description.abstract | 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. | - |
dc.description.sponsorship | This work was jointly supported by the Korea Research Foundation Grant funded by the Korean Government (KRF- 2005-213-D00092), the Smart Infra- Structure Technology Center at KAIST sponsored by the Korea Science and Engineering Foundation, and the Infra-Structure Assessment Research Center sponsored by Ministry of Construction and Transportation, Korea. This material is also based upon work supported by the National Science Foundation under Grant No. CMS 0120827. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This financial support is greatly appreciated. Finally, the authors would like to thank Dr Gyuhae Park of Los Alamos National Laboratory for giving a kind guidance for the experiment. | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.title | Electro-mechanical impedance-based wireless structural health monitoring using PCA-data compression and k-means clustering algorithms | - |
dc.type | Article | - |
dc.identifier.wosid | 000254845700008 | - |
dc.identifier.scopusid | 2-s2.0-40949126904 | - |
dc.type.rims | ART | - |
dc.citation.volume | 19 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 509 | - |
dc.citation.endingpage | 520 | - |
dc.citation.publicationname | JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES | - |
dc.identifier.doi | 10.1177/1045389X07077400 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Yun, Chung Bang | - |
dc.contributor.nonIdAuthor | Lee, Jong-Jae | - |
dc.contributor.nonIdAuthor | Inman, Daniel J. | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | electromechanical impedance | - |
dc.subject.keywordAuthor | wireless | - |
dc.subject.keywordAuthor | structural health monitoring | - |
dc.subject.keywordAuthor | on-board active sensor system | - |
dc.subject.keywordAuthor | self-sensing macro-fiber composite patch | - |
dc.subject.keywordAuthor | principal component analysis | - |
dc.subject.keywordAuthor | k-means clustering | - |
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