A new orthogonal neighborhood preserving embedding (ONPE) and its kernel generalization based process monitoring approaches are presented in this paper. ONPE aims at preserving local neighborhood structure of process data while reducing data dimensionality. As an approximation of the nonlinear manifold learning method, ONPE is capable to handle process nonlinearity. Moreover, to enhance the nonlinear modeling performance, the nonlinear extension of ONPE is also developed, with the introduction of kernel-tricks. By constructing monitoring statistics, both ONPE and its generalization are applied for fault detection in nonlinear processes. Two case studies show the superiority of the proposed methods in process monitoring.
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