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Smart Structures and Systems
  Volume 20, Number 2, August 2017, pages 219-229
DOI: https://doi.org/10.12989/sss.2017.20.2.219
 


Outlier detection of GPS monitoring data using relational analysis and negative selection algorithm
Ting-Hua Yi, X.W. Ye, Hong-Nan Li and Qing Guo

 
Abstract
    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.
 
Key Words
    structural health monitoring; global positioning system; outlier detection; grey relational analysis; negative selection algorithm
 
Address
Ting-Hua Yi, Hong-Nan Li and Qing Guo: School of Civil Engineering, Dalian University of Technology, Dalian 116023, China
X.W. Ye: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
 

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