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.

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

Zio, Enrico
2019-01-01

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.
2019
Auto Associative Kernel Regression; Bilateral kernel; Nuclear power plant; Process monitoring; Transient operation; Instrumentation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineering; Applied Mathematics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1077929
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