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Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults

Author

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  • Niu, Yichun
  • Gao, Ming
  • Sheng, Li

Abstract

In this paper, the problem of distributed fault-tolerant state estimation is studied for stochastic systems over sensor networks with intermittent sensor faults. Compared with the traditional state estimation algorithms, the distinct advantage of fault-tolerant state estimation is that the estimator can keep good performance whether sensor faults occur or not. Different from the previous literature concerning with distributed fault diagnosis, the distributed fault diagnosis problem is investigated in this paper for intermittent faults, whose appearing time, disappearing time and magnitude are all nondeterministic. The distributed fault-tolerant state estimation scheme is constructed, in which the appearing time and disappearing time of intermittent faults are detected, intermittent faults are estimated and compensated. By means of the matrix inequality technique, the H∞ performance of state estimation errors is guaranteed by properly choosing the estimator parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed algorithm.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:416:y:2022:i:c:s0096300321008079
    DOI: 10.1016/j.amc.2021.126723
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    References listed on IDEAS

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    1. Li, Qian & Liu, Xinzhi & Zhu, Qingxin & Zhong, Shouming & Zhang, Dian, 2019. "Distributed state estimation for stochastic discrete-time sensor networks with redundant channels," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 230-246.
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    Cited by:

    1. Jianing Cao & Hua Chen, 2023. "Mathematical Model for Fault Handling of Singular Nonlinear Time-Varying Delay Systems Based on T-S Fuzzy Model," Mathematics, MDPI, vol. 11(11), pages 1-13, June.
    2. 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).
    3. Miao, Suoxia & Su, Housheng, 2024. "Behaviors of matrix-weighted networks with antagonistic interactions," Applied Mathematics and Computation, Elsevier, vol. 467(C).

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