Advanced data analysis and process monitoring across manufacturing plants using multilevel methods - de Noord, Onno E. (Journal of chemometrics, 2016)
Three‐level versions of Multilevel Simultaneous Component Analysis (MLSCA) and Multilevel Partial Least Squares (MLPLS) were developed, which are capable of separating between‐plant, between‐run and within‐run process variation, and modeling these three levels in a multivariate way. In comparison to the two‐level versions they allow to discriminate between overall differences between plants and the variation between runs within a plant. It was shown that the three‐level version of MLSCA has clear added value for the analysis of process runs from different plants. In MLPLS other projections of the multivariate data onto latent variables and different views of the data are obtained when relevant Y information is available. This has clear added value for obtaining insight into the relation between process data and Y. A special use of MLPLS is to diagnose aberrations in first principles models. In batch process monitoring MLSCA at three levels allows simultaneous multivariate modelling of batch data from different manufacturing plants. By filtering out the between‐plant and between‐run sources of variation, and using only within‐run variation, monitoring models can be improved. Using within‐run data, it is possible to build monitoring models across manufacturing units and reduce the number of nuisance alarms, while improving abnormal situation detection and diagnosis. Model transfer is only possible if static between‐plant differences exist, but not if there are dynamic differences. Multilevel Simultaneous Component Analysis (MLSCA) and Multilevel Partial Least Squares (MLPLS) have been extended to three levels. This allows the separation of process data into between‐plant, between‐run and within‐run sources of variation, which has great benefits for multivariate process data analysis and process monitoring. The methods can be applied to data from continuous and batch processes.