Industrial batch processes are widely used in the production of certain items. Such processes provide a peculiar data structure; therefore there is a growing interest in the development of customized multivariate control charts for their monitoring. We investigate a recent approach that uses control charts based on the Statis method. Statis is an exploratory technique for measuring similarities between data matrices. However, the technique only assesses similarities in a linear context, i.e. investigating structures of linear correlation in the data. In this paper we propose control charts based on the Statis method in conjunction with a kernel for monitoring processes in the presence of strong nonlinearities. Through kernels we define nonlinear functions of data for better representing the structure to be characterized by the Statis method. The new approach, named kernel-Statis, is developed and illustrated using simulated data.
Multivariate quality control; Control charts; Batch processes; kernel; Statis method
