Smart Structures and Systems

  • 제20권2호
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  • Pages.219-229
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  • 2017
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Outlier detection of GPS monitoring data using relational analysis and negative selection algorithm

  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Ye, X.W. (Department of Civil Engineering, Zhejiang University) ;
  • Li, Hong-Nan (School of Civil Engineering, Dalian University of Technology) ;
  • Guo, Qing (School of Civil Engineering, Dalian University of Technology)
  • 투고 : 2016.12.30
  • 심사 : 2017.04.18
  • 발행 : 2017.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China, Central Universities of China

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