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Quantitative analysis of incipient fault detectability for time-varying stochastic systems based on weighted moving average approach

Author

Listed:
  • Gao, Ming
  • Niu, Yichun
  • Sheng, Li
  • Zhou, Donghua

Abstract

In this paper, the problem of incipient fault detection is investigated for linear time-varying (LTV) systems with stochastic noises. The fault detectability in a probabilistic sense is defined for LTV stochastic systems by considering false alarm rate (FAR) and missed detection rate (MDR) simultaneously. Necessary and sufficient conditions are derived to reveal the relationship among the fault amplitude, FAR and MDR, and the reason why incipient faults are difficult to detect is quantitatively analyzed in the model-based framework. To improve the sensibility of the residual to incipient faults, the weighted moving average approach is introduced and its parameters, the optimal weight and the smallest window length, are accurately analyzed in theory. Moreover, the concept of average fault detectability is introduced, which is conducive to providing a feasible scheme for incipient fault detection. Finally, a numerical example and an experiment are given to show the effectiveness of the derived results.

Suggested Citation

  • Gao, Ming & Niu, Yichun & Sheng, Li & Zhou, Donghua, 2022. "Quantitative analysis of incipient fault detectability for time-varying stochastic systems based on weighted moving average approach," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    • Handle: RePEc:eee:apmaco:v:434:y:2022:i:c:s009630032200546x
    DOI: 10.1016/j.amc.2022.127472
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    References listed on IDEAS

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    1. Ma, Zhen-Lei & Li, Xiao-Jian, 2022. "Data-driven fault detection for large-scale network systems: A mixed optimization approach," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    2. Lv, Yuan-Wei & Yang, Guang-Hong, 2022. "An adaptive cubature Kalman filter for nonlinear systems against randomly occurring injection attacks," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    3. Zhang, Zhi-Hui & Hao, Li-Ying & Guo, Mingjie, 2022. "Fault detection for uncertain nonlinear systems via recursive observer and tight threshold," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    4. Niu, Yichun & Gao, Ming & Sheng, Li, 2022. "Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults," Applied Mathematics and Computation, Elsevier, vol. 416(C).
    5. Kang, Haobo & Ma, Hongjun, 2022. "Fault detection and isolation of actuator failures in jet engines using adaptive dynamic programming," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    6. Yamei Ju & Xin Tian & Hongjian Liu & Lifeng Ma, 2021. "Fault detection of networked dynamical systems: a survey of trends and techniques," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(16), pages 3390-3409, December.
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