| Abstract
| - An adaptive process monitoring approach with variable moving window principal component analysis (variableMWPCA) is proposed. On the basis of recursively updating the correlation matrix in both samplewise andblockwise manners, the approach combines the moving window technique with the classical rank-r singularvalue decomposition (R-SVD) algorithm to construct a new PCA model. Compared with previous MWPCAalgorithms, the method not only improves the computation efficiency but also reduces the storage requirement.Furthermore, instead of a fixed window size, a variable moving window strategy is described in detail foraccommodating normal process changes with different changing rates. The proposed method is applied to anillustrative case and a continuous stirred tank reactor process, and the monitoring results show better adaptabilityto both a slow drift and a set-point change than the results of using the conventional MWPCA with a fixedwindow size.
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