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Modified partial least squares (MPLS) is an efficient tool widely used in multivariate statistical process monitoring. To properly describe slow time-varying processes, the method commonly used in model updating is data expansion. However, when the number of lagged variables grows, the modeling order and computational load increase significantly. A recursive structure that has a low computational complexity is an efficient way to update models. However, the recursive structure fails to reduce the false alarm rates (FARs) of quality-unrelated faults when model updating. To cope with this problem, a recursive MPLS (RMPLS) detection approach based on orthogonal signal correction (OSC), named OSC-RMPLS here, is proposed. OSC-RMPLS removes the variation orthogonal to the output space Y from the input space X before MPLS modeling, decomposes X into two orthogonal subspaces, and further updates the RMPLS model adaptively. Compared with the data expansion method, the proposed algorithm has a lower computational load and more robust performance. Numerical simulation experiments and Tennessee Eastman experiments (TEP) are used to verify the effectiveness of the proposed algorithm.

Quality-Relevant Data-Driven Process Monitoring Based on Orthogonal Signal Correction and Recursive Modified PLS