Principal component analysis, contribution plots and feature weights in the monitoring of sequential process data from a paper machineʹs wet end

Data collected from a paper mill using a WIC-100 process analyzer was divided into six classes, each representing a different kind of paper grade or quality. Each of the six classes were modeled separately by principal component analysis (PCA). The score values of the calibration data, together with the corresponding confidence limits and the trajectory of the current data, are used to visualize the state of the process. For each of the classes, two collective multivariate control charts have been used to describe the state of the process. The first one of these charts is calculated from the residuals and the second one is based on the Mahalanobis distance of the score values. Both of these charts can be traced back to the original variables. Multivariate control charts and biplots have been applied together with the contribution plots and the feature weights in order to detect any process problems and to isolate the deviating variables. The results have been verified by using parallel coordinates. These methods are useful in detecting and isolating the various types of changes that may occur in the wet end process of a paper machine. The concept of contribution map has also been introduced. In this context, Bonferroni bounds have been used as decision rules for plotting points (warnings) on the contribution map.