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Decentralized fault detection and isolation using bond graph and PCA methods

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Abstract

This paper proposes a new method for fault detection and isolation of a large-scale system by the bond graph (BG) model and the principal component analysis (PCA) technique in a decentralized architecture. The proposed method is entitled the BG-PCA technique. The main objective is to address the problem of monitoring large-scale system components by fault detection and isolation (FDI) methods. In the modeling framework, the BG model is presented by exploiting its structural and causal properties and the diagnostic bond graph (DBG). In measurement noise and perturbation conditions, the probability density function takes place to generate a clear decision procedure to detect the operating mode. In our approach, we firstly generate the structured residues. Furthermore, with a decentralized architecture, we can locate in what subsystem a fault is first detected. Finally, for isolation, the generation of the structured residues method from PCA is exploited to ensure localization. To validate the suggested BG-PCA algorithm, simulations are computed on a three-tank system to show the efficiency of the proposed FDI method, and satisfactory results are found.

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Authors and Affiliations

  1. Research Laboratory of Automation, Signal Processing and Image (LARATSI), National School of Engineering Sousse, University of Sousse, Sousse Erriadh, 4023, Sousse, Tunisia

    Maroua Said

  2. Research Laboratory of Automation, Signal Processing and Image (LARATSI), National School of Engineering Monastir, University of Monastir, Rue Ibn ELJazzar, 5019, Monastir, Tunisia

    Radhia Fazai & Okba Taouali

  3. Research Laboratory Mars, ISITCom, University of Sousse, Sousse, Tunisia

    Khaoula Ben Abdellafou

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  1. Maroua Said
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  2. Radhia Fazai
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  3. Khaoula Ben Abdellafou
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  4. Okba Taouali
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Correspondence to Okba Taouali.

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Said, M., Fazai, R., Ben Abdellafou, K. et al. Decentralized fault detection and isolation using bond graph and PCA methods. Int J Adv Manuf Technol 99, 517–529 (2018). https://doi.org/10.1007/s00170-018-2526-4

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  • DOI: https://doi.org/10.1007/s00170-018-2526-4

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