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Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series

Author

Listed:
  • Bingjian Wu

    (SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai 201800, China
    SILC Business School, Shanghai University, Shanghai 201800, China)

  • Fan Zhang

    (SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai 201800, China
    SILC Business School, Shanghai University, Shanghai 201800, China)

  • Yi Wang

    (SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai 201800, China
    SILC Business School, Shanghai University, Shanghai 201800, China)

  • Min Hu

    (SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai 201800, China
    SILC Business School, Shanghai University, Shanghai 201800, China)

  • Xue Bai

    (SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai 201800, China
    SILC Business School, Shanghai University, Shanghai 201800, China)

Abstract

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

Suggested Citation

  • Bingjian Wu & Fan Zhang & Yi Wang & Min Hu & Xue Bai, 2024. "Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series," Sustainability, MDPI, vol. 16(8), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3335-:d:1376661
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    References listed on IDEAS

    as
    1. Wenqing Wang & Junpeng Bao & Tao Li, 2021. "Correction to: Bound smoothing based time series anomaly detection using multiple similarity measures," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1791-1791, August.
    2. Wenqing Wang & Junpeng Bao & Tao Li, 2021. "Bound smoothing based time series anomaly detection using multiple similarity measures," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1711-1727, August.
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