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Dynamic event-triggered fault detection based on partially observed modes for hidden semi-Markov jump systems

Author

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  • Qiyi Wang
  • Yang Bai
  • Li Peng

Abstract

This paper addresses the fault detection problem for hidden semi-Markov jump systems with partially observed modes, which is a challenging yet critical issue in systems with dynamic and uncertain mode observations. Unlike standard semi-Markov models, hidden semi-Markov models allow for more complex dynamics but introduce difficulties in analysing mode-dependent behaviours due to unobservable modes. To tackle this, a partially mode-dependent fault detection filter is developed, incorporating both mode-independent and mode-dependent parameters, thereby accommodating extreme cases where observed modes are either always lost or consistently available. Furthermore, a novel dynamic event-triggered mechanism is proposed, featuring an adjustable parameter to balance fault detection performance and system transmission load. The filter design is guided by a set of derived inequality conditions to compute the filter gains. Numerical simulations validate the proposed approach, demonstrating its effectiveness and robustness in handling diverse scenarios with varying levels of observability and transmission constraints.

Suggested Citation

  • Qiyi Wang & Yang Bai & Li Peng, 2026. "Dynamic event-triggered fault detection based on partially observed modes for hidden semi-Markov jump systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 57(5), pages 1446-1458, April.
    • Handle: RePEc:taf:tsysxx:v:57:y:2026:i:5:p:1446-1458
    DOI: 10.1080/00207721.2025.2530664
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