Cluster analysis for multivariable process control

This paper describes the novel use of cluster analysis in the field of industrial process control. The severe multivariable process problems encountered in manufacturing have often led to machine shutdowns, where the need for corrective actions arises in order to resume operation. Production faults which are caused by processes running in less efficient regions may be prevented or diagnosed using reasoning based on cluster analysis. Indeed the internal complexity of a production machinery may be depicted in clusters of multidimensional data points which characterise the manufacturing process. The application of a mean-tracking cluster algorithm to field data acquired from a high-speed machinery is discussed. The objective of such an application is to illustrate how machine behaviour can be studied, in particular how regions of erroneous and stable running behaviour can be identified.