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On-line process monitoring during transient operations using weighted distance Auto Associative Bilateral Kernel Regression

Abstract : In this paper, a new data-driven auto associative bilateral kernel regression (AABKR) method based on weighted distance is proposed for the on-line monitoring of transient process operations. A bilateral approach to the kernel regression formulates a representative model that uses both the spatial and temporal information in the data, and a new weighted-distance algorithm captures temporal information. Moreover, an adaptive approach is proposed to dynamically compensate for faulty process inputs in the bilateral kernel evaluations, providing a robust model with little spillover. The proposed weighted-distance AABKR is first implemented using numerical process examples and then applied to the transient start-up operation of a nuclear power plant. Monte Carlo simulation results are provided by randomly assigning fault sensors and fault magnitudes. The results demonstrate the feasibility and efficiency of the proposed method.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-02432427
Contributeur : Magalie Prudon <>
Soumis le : mercredi 8 janvier 2020 - 14:18:16
Dernière modification le : jeudi 24 septembre 2020 - 17:20:28

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Ibrahim Ahmed, Gyunyoung Heo, Enrico Zio. On-line process monitoring during transient operations using weighted distance Auto Associative Bilateral Kernel Regression. ISA Transactions, Elsevier, 2019, 92, pp.191-212. ⟨10.1016/j.isatra.2019.02.010⟩. ⟨hal-02432427⟩

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