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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
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Submitted on : Wednesday, January 8, 2020 - 2:18:16 PM
Last modification on : Wednesday, December 2, 2020 - 1:14:15 PM

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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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