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An outlier analysis of MFC-based impedance sensing data for wireless structural health monitoring of railroad tracks
AbstractThis paper presents an outlier analysis for damage detection of railroad tracks using a macro-fiber composite (MFC) impedance-based wireless structural health monitoring (SHM) system. The impedance-based SHM method has some limitations because the measured impedance data may have considerable deviations caused by environmental or operational condition changes, including temperature, humidity, external loadings, or MFC patch bonding conditions. Thus, the method sometimes gives false-positive indication even for healthy structures. In order to overcome this limitation, an outlier analysis based on Mahalanobis squared distance (MSD) was proposed by taking root mean square deviation (RMSD) values of impedance signatures as a damage-sensitive feature vector. Optimal threshold values for both RMSD and MSD were determined through the proposed outlier analysis. The results showed that the use of MSD improved the damage detection capability with a lower threshold level as compared to that of RMSD. In this study, the applicability of the proposed method was experimentally verified by detecting three types of the railroad track damage, including head damage, web damage, and flange damage, which were simulated under the laboratory setting.
An outlier analysis of MFC-based impedance sensing data for wireless structural health monitoring of railroad tracks
AbstractThis paper presents an outlier analysis for damage detection of railroad tracks using a macro-fiber composite (MFC) impedance-based wireless structural health monitoring (SHM) system. The impedance-based SHM method has some limitations because the measured impedance data may have considerable deviations caused by environmental or operational condition changes, including temperature, humidity, external loadings, or MFC patch bonding conditions. Thus, the method sometimes gives false-positive indication even for healthy structures. In order to overcome this limitation, an outlier analysis based on Mahalanobis squared distance (MSD) was proposed by taking root mean square deviation (RMSD) values of impedance signatures as a damage-sensitive feature vector. Optimal threshold values for both RMSD and MSD were determined through the proposed outlier analysis. The results showed that the use of MSD improved the damage detection capability with a lower threshold level as compared to that of RMSD. In this study, the applicability of the proposed method was experimentally verified by detecting three types of the railroad track damage, including head damage, web damage, and flange damage, which were simulated under the laboratory setting.
An outlier analysis of MFC-based impedance sensing data for wireless structural health monitoring of railroad tracks
Park, Seunghee (author) / Inman, Daniel J. (author) / Yun, Chung-Bang (author)
Engineering Structures ; 30 ; 2792-2799
2008-02-29
8 pages
Article (Journal)
Electronic Resource
English
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