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Detecting scalar intermittent faults in linear stochastic dynamic systems

Author

Listed:
  • Maoyin Chen
  • Guibin Xu
  • Rongyi Yan
  • Steven X. Ding
  • Donghua Zhou

Abstract

Intermittent faults (IFs) have properties such as intermittency, random magnitude and random duration time. Hence the detection of IFs means: (i) to detect not only all the appearing time but also all the disappearing time of IFs and (ii) to detect the appearing time of an IF before this IF disappears, and the disappearing time of an IF before the subsequent IF appears. Within a statistical framework, the detection of scalar IFs in continuous linear stochastic dynamic systems has been mainly studied. Based on the sliding window, an analytical residual is generated, and two hypothesis tests are implemented to detect the appearing and disappearing times of IFs. In addition, a necessary and sufficient condition for the detectability of IFs is obtained, and the detection speed can be fast enough. Theoretical analysis and numerical simulations fully verify that IFs can be successfully detected.

Suggested Citation

  • Maoyin Chen & Guibin Xu & Rongyi Yan & Steven X. Ding & Donghua Zhou, 2015. "Detecting scalar intermittent faults in linear stochastic dynamic systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(8), pages 1337-1348, June.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:8:p:1337-1348
    DOI: 10.1080/00207721.2013.809612
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    References listed on IDEAS

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    1. Samir Khan & Roger Goodall & Roger Dixon, 2013. "Non-uniform sampling strategies for digital control," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(12), pages 2234-2254.
    2. L. Blázquez & Luis de Miguel & Fernando Aller & José Perán, 2011. "Neuro-fuzzy identification applied to fault detection in nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(10), pages 1771-1787.
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    Cited by:

    1. Sheng, Li & Zhang, Sen & Gao, Ming, 2021. "Intermittent fault detection for linear discrete-time stochastic multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 410(C).

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