Pseudo-sample based contribution plots: innovative tools for fault diagnosis in kernel-based batch process monitoring

This article explores the potential of kernel-based methods for fault diagnosis in batch process monitoring by combining Kernel-Principal Component Analysis and three common techniques which permit analyzing batch data by means of bilinear models: variable-wise-unfolding, batch-wise unfolding and landmark feature extraction. Gower's idea of pseudo-sample projection is exploited to develop novel tools, the pseudo-sample based contribution plots, for diagnostic purposes. The results show that when the datasets under study are affected by severe non-linearities, the proposed approach performs better than classical ones.