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In this paper, fault detection and identification methods based on semi‐supervised Laplacian regularization kernel partial least squares (LRKPLS) are proposed. In Laplacian regularization learning framework, unlabeled and labeled samples are used to improve estimate of data manifold so that one can establish a more robust data model. We show that LRKPLS can avoid the over‐fitting problem which may...
In this paper, a novel fault monitoring and diagnosis approach based on kernel partial least squares(KPLS) is introduced. Unlike other nonlinear least squares (PLS) techniques, KPLS does not consider any nonlinear systems optimization procedures and has the characteristics similar to that of linear PLS. In this paper, KPLS provides good monitoring performance by finding those latent variables that...
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