Process performance monitoring using multivariate statistical process control

Statistical process control (SPC) is a tool for achieving and maintaining product quality. Classical univariate statistical techniques have focused on the monitoring of one quality variable at a time and are not appropriate for analysing process data where variables exhibit collinear behaviour. Minimal information is derived on the interactions between variables which are so important in complex manufacturing processes. These limitations are addressed through the application of multivariate statistical process control (MSPC). The bases of MSPC are the projection techniques of principal components analysis and projection to latent structures. The philosophy behind these approaches is to reduce the dimensionality of the problem by forming a new set of latent variables to obtain an enhanced understanding of the process behaviour. If the variables are highly correlated, then the process can be defined in terms of a reduced set of latent variables, which are a linear combination of the original variables. The authors present an overview of multivariate statistical process control and its nonlinear extension for process monitoring. The power of the methodology is demonstrated by application to two industrial processes.