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

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.