Smart Structures and Systems

  • 제5권4호
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  • Pages.457-468
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  • 2009
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Developing an integrated software solution for active-sensing SHM

  • Overly, T.G. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Jacobs, L.D. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Farinholt, K.M. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Park, G. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Farrar, C.R. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Flynn, E.B. (Department of Structural Engineering, University of California) ;
  • Todd, M.D. (Department of Structural Engineering, University of California)
  • 투고 : 2008.10.07
  • 심사 : 2008.10.20
  • 발행 : 2009.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

키워드

참고문헌

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피인용 문헌

  1. Use of Relative Baseline Features of Guided Waves for In situ Structural Health Monitoring vol.22, pp.2, 2011, https://doi.org/10.1177/1045389X10395643