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Online Data Fusion Using Incremental Tensor Learning

  • Nguyen Lu Dang KhoaEmail author
  • Hongda Tian
  • Yang Wang
  • Fang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

Abstract

Despite the advances in Structural Health Monitoring (SHM) which provides actionable information on the current and future states of infrastructures, it is still challenging to fuse data properly from heterogeneous sources for robust damage identification. To address this challenge, the sensor data fusion in SHM is formulated as an incremental tensor learning problem in this paper. A novel method for online data fusion from heterogeneous sources based on incrementally-coupled tensor learning has been proposed. When new data are available, decomposed component matrices from multiple tensors are updated collectively and incrementally. A case study in SHM has been developed for sensor data fusion and online damage identification, where the SHM data are formed as multiple tensors to which the proposed data fusion method is applied, followed by a one-class support vector machine for damage detection. The effectiveness of the proposed method has been validated through experiments using synthetic data and data obtained from a real-life bridge. The results have demonstrated that the proposed fusion method is more robust to noise, and able to detect, assess and localize damage better than the use of individual data sources.

Keywords

Data fusion Incrementally-coupled tensor learning Online learning Anomaly detection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nguyen Lu Dang Khoa
    • 1
    Email author
  • Hongda Tian
    • 2
  • Yang Wang
    • 2
  • Fang Chen
    • 2
  1. 1.Data61, CSIROSydneyAustralia
  2. 2.University of Technology SydneySydneyAustralia

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