• DocumentCode
    1599581
  • Title

    Adaptive and Online Fault Detection Using RPCA Algorithm in Wireless Sensor Network Nodes

  • Author

    Yingxin Xie ; Xiangguang Chen ; Jun Zhao

  • Author_Institution
    Sch. of Chem. Eng. & Environ., Beijing Inst. of Technol., Beijing, China
  • fYear
    2012
  • Firstpage
    1371
  • Lastpage
    1374
  • Abstract
    A wide range of applications have started to use Wireless Sensor Network (WSN) as an information collection and monitoring tool. Reliable and accurate performance of sensor nodes is necessary in critical applications. For characteristics of the WSN data stream environment, the limitations of conventional principal component analysis (PCA) method which depend on the fixed model in practical application are analyzed, an online fault detection framework in WSN nodes based on recursive PCA (RPCA) model is proposed. The algorithm applies RPCA algorithm to sequentially update the model representing normal behavior of the sensed data adaptively and realize the online fault detection. Experiments with real data show that our online fault detection algorithm not only tracks the normal changes well, but also achieves good detection performance for typical node faults.
  • Keywords
    fault diagnosis; principal component analysis; wireless sensor networks; PCA; RPCA algorithm; WSN nodes; node faults; online fault detection; principal component analysis; wireless sensor network nodes; Adaptation models; Covariance matrix; Data models; Fault detection; Monitoring; Principal component analysis; Wireless sensor networks; data stream; fault detection; recursive principal component analysis; wireless sensor network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on
  • Conference_Location
    Sanya, Hainan
  • Print_ISBN
    978-1-4577-2120-5
  • Type

    conf

  • DOI
    10.1109/ISdea.2012.735
  • Filename
    6173464