DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, S | ko |
dc.contributor.author | Lee, JJ | ko |
dc.contributor.author | Yun, Chung Bang | ko |
dc.contributor.author | Inman, DJ | ko |
dc.date.accessioned | 2008-10-15T06:06:21Z | - |
dc.date.available | 2008-10-15T06:06:21Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2007-06 | - |
dc.identifier.citation | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.21, pp.896 - 902 | - |
dc.identifier.issn | 1738-494X | - |
dc.identifier.uri | http://hdl.handle.net/10203/7664 | - |
dc.description.abstract | A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole-damage 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature 1: root mean square deviations (RMSD) of impedance signatures, and b) feature II : sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes (OSH) were successfully established by the two-step SVM classifier: Damage detection was accomplished by the first step-SVM, and damage classification was carried out by the second step-SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by thirty test patterns prepared in advance from the intact state and two damage states. | - |
dc.description.sponsorship | The work was jointly supported by the Smart Infra- Structure Technology Center (SISTeC) at KAIST, by the Korea Science and Engineering Foundation and the Infra-Structure Assessment Research Center (ISARC), the Ministry of Construction and Transportation, Korea, and the Railway Tech Laboratories of The United States. This financial support is greatly appreciated. | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | KOREAN SOC MECHANICAL ENGINEERS | - |
dc.title | A built-in active sensing system-based structural health monitoring technique using statistical pattern recognition | - |
dc.type | Article | - |
dc.identifier.wosid | 000247381900012 | - |
dc.identifier.scopusid | 2-s2.0-34250890340 | - |
dc.type.rims | ART | - |
dc.citation.volume | 21 | - |
dc.citation.beginningpage | 896 | - |
dc.citation.endingpage | 902 | - |
dc.citation.publicationname | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Yun, Chung Bang | - |
dc.contributor.nonIdAuthor | Park, S | - |
dc.contributor.nonIdAuthor | Lee, JJ | - |
dc.contributor.nonIdAuthor | Inman, DJ | - |
dc.type.journalArticle | Article; Proceedings Paper | - |
dc.subject.keywordAuthor | structural health moniboring | - |
dc.subject.keywordAuthor | support vector machine classifier | - |
dc.subject.keywordAuthor | rail road track damage identification | - |
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