Process performance monitoring in the presence of confounding variation

The aim of this paper is to present a two-stage partial least squares (PLS) methodology, for the monitoring of processes that are known to be affected by sources of variation that are an inherent part of routine operations. These sources, termed nuisance or confounding variation, will typically either mask the more subtle process changes that are of particular interest to operational personnel, or make the determination of the source of non-conforming operation more difficult to locate. In the first stage of the two-stage algorithm, the sources of confounding variation are extracted and removed through the application of a PLS based filter. The second stage takes the filtered ‘signal’ and through the application of PLS those latent variables that are uncorrelated with the nuisance source of variation are extracted. These latent variables then form the basis of a multivariate statistical process control model. The algorithm is compared with the ordinary PLS based performance monitoring and a monitoring scheme that is formed from the latent variables of a PLS model developed after the application of Orthogonal Signal Correction (OSC). The methodologies are illustrated and compared by application to a mathematical simulation example and an industrial semi-discrete batch manufacturing process. In both cases it is shown that the two-stage PLS algorithm is more able to detect and locate the sources of subtle process changes.