Process monitoring in principal component subspace: part 1 - fault reconstruction study

The principal component analysis (PCA) is a kind of data-driven modeling method that has wide applications in the field of industrial process monitoring and product quality control. However, it was shown that some faults can only be detected in the principal component subspace (PCS) and the T² statistic in PCS is more robust than SPE statistic while the latter is in the residual subspace (RS). A reconstruction approach for these faults in the PCS is proposed to estimate the fault magnitude and then judge its type. The reconstructability conditions both for complete and partial ones are derived mathematically and the obtained results are illustrated and verified by simulation studies on a double-effective evaporator.