Smart Structures and Systems

  • 제29권1호
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  • Pages.93-103
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  • 2022
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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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Data anomaly detection for structural health monitoring of bridges using shapelet transform

  • Arul, Monica (Nathaz Modeling Laboratory, Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame) ;
  • Kareem, Ahsan (Nathaz Modeling Laboratory, Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame)
  • 투고 : 2021.04.13
  • 심사 : 2021.07.29
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

키워드

과제정보

This work was supported in part by the Robert M. Moran Professorship and National Science Foundation Grant (CMMI 1612843). The authors would like to thank the organizers of the International Project Competition for Structural Health Monitoring (IPC-SHM 2020), Asia-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), Harbin Institute of Technology (China), and the University of Illinois at Urbana-Champaign (USA) for providing the structural health monitoring data of the long-span bridge. The authors also would like to thank the chairs of IPC-SHM 2020, Prof. Hui Li and Prof. Billie F. Spencer Jr, for their leadership in the competition.

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