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Fault Estimator and Diagnosis for Generalized Linear Discrete-Time System via Self-constructing Fuzzy UKF Method

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Abstract

This study investigated fault estimation and diagnosis using a novel approach based on an integrated fault estimator and state estimator for generalized linear discrete-time systems. The proposed scheme uses a self-constructing fuzzy unscented Kalman filter (UKF) system to simultaneously estimate the system state and approximate the fault information. To achieve this, a generalized linear discrete-time system without faults was first transformed into an equivalent standard state-space system with faults. Then, the self-constructing fuzzy UKF system was designed in order to obtain the fault information. According to fault information obtained using the proposed scheme, fault detection experiments based on fuzzy clustering were performed and the fault feature parameters required for fault isolation were determined. Finally, the scheme was applied to a direct current (DC) motor to demonstrate the effectiveness of the proposed fault estimation and diagnosis approach. Results of the simulation illustrate the effectiveness of the proposed method.

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Funding

Funding was provided by National Natural Science Foundation of China (Grant No. 51675398) and National Key Basic Research Program of China (Grant No. 2015CB857100).

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Authors and Affiliations

  1. Key Laboratory of Electronic Equipment Structure Design Ministry of Education, Xidian University, Xi’an, 710071, China

    Zhiyong Liu, Hong Bao, Song Xue & Jingli Du

  2. Xian Yang Vocational Technical College, Xian Yang, 712000, China

    Zhiyong Liu

Authors
  1. Zhiyong Liu
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  2. Hong Bao
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  3. Song Xue
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  4. Jingli Du
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Corresponding author

Correspondence to Hong Bao.

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Liu, Z., Bao, H., Xue, S. et al. Fault Estimator and Diagnosis for Generalized Linear Discrete-Time System via Self-constructing Fuzzy UKF Method. Int. J. Fuzzy Syst. 22, 232–241 (2020). https://doi.org/10.1007/s40815-019-00750-7

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  • DOI: https://doi.org/10.1007/s40815-019-00750-7

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