Abstract

Industrial batch processing is widely used in a number of areas of industrial production. Data arising from such processes may present special characteristics; there is therefore a growing interest in the development of customized multivariate control charts for their monitoring. Here 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 nonlinearities. Through kernels we define nonlinear functions of data giving better representation of the structure to be characterized by the Statis method. The new approach is illustrated using simulated data.