Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 54, Issue 9, Pages 2509-2518Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ie501502t
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In this paper, a directional kernel partial least squares (DKPLS) monitoring method is proposed. The contributions are as follows: (1) By analysis of the relevance between the input residual and output variables, the kernel partial least squares (KPLS) residual subspace still contains output-relevant variation. (2) A new KPLS algorithm, DKPLS, is proposed to extract the output-relevant variation. Compared with the conventional algorithm, the DKPLS algorithm builds a more direct relationship between the input and output variables. (3) On the basis of the proposed DKPLS algorithm, a process monitoring method is proposed. In this monitoring method, kernel latent variables are used to explain the extracted output-relevant variation and calculate monitoring indices. Faults are detected accurately by the proposed DKPLS method. The DKPLS monitoring method is used to monitor a numerical example and the electrofused magnesium process. The experimental results show the effectiveness of the proposed DKPLS method.
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