Smart Structures and Systems

  • 제5권4호
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  • Pages.415-426
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  • 2009
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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BRAIN: A bivariate data-driven approach to damage detection in multi-scale wireless sensor networks

  • Kijewski-Correa, T. (Dept. of Civil Engineering & Geological Sciences, University of Notre Dame) ;
  • Su, S. (Dept. of Civil Engineering & Geological Sciences, University of Notre Dame)
  • 투고 : 2008.08.20
  • 심사 : 2008.09.28
  • 발행 : 2009.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

키워드

과제정보

연구 과제 주관 기관 : Department of Defense

참고문헌

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  3. Kijewski-Correa, T., Haenggi, M. and Antsaklis, P. (2006a), "Wireless sensor networks for structural health monitoring: a multi-scale approach", Proc. of 2006 ASCE Structures Congress, 17th Analysis & Computation Specialty Conf., St. Louis, May.
  4. Kijewski-Correa, T., Su, S., Abittan, E. and Antsaklis, P.J. (2006b), "On the use of heterogeneous, wireless sensor networks for damage assessment in bridges under unknown excitations", Proc. of 4th World Conf. on Structural Control and Monitoring, San Diego, July.
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  9. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure", J. Sound Vib., 291(3), 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
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  13. Su, S. and Kijewski-Correa, T. (2007a), "Performance verification of bivariate regressive adaptive index for structural health monitoring", Proc. of SPIE Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, San Diego, March.
  14. Su, S. and Kijewski-Correa, T. (2007b), "On the use of a bivariate regressive adaptive index for structural health monitoring", Proc. of Structural Health Monitoring and Intelligent Infrastructure (SHM-II) 2007, Vancouver, November.
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피인용 문헌

  1. Structural monitoring of wind turbines using wireless sensor networks vol.6, pp.3, 2009, https://doi.org/10.12989/sss.2010.6.3.183
  2. Rapid-to-deploy reconfigurable wireless structural monitoring systems using extended-range wireless sensors vol.6, pp.5, 2009, https://doi.org/10.12989/sss.2010.6.5_6.505
  3. Compressed sensing-based electromechanical admittance data loss recovery for concrete structural health monitoring vol.20, pp.3, 2009, https://doi.org/10.1177/1475921720950640