Enhancing quality of statistic monitoring models by training set design with active learning approach

Abstract

The objective of this paper is to enhance the quality of the process monitoring models by designing a training set through an active learning approach. Although conventional process monitoring models are effective in many manufacturing processes, these models falter when confronted by a set of training data with poor quality or a small volume of training data. As the limitations of the monitoring models become increasingly obvious in face of even more complex manufacturing processes, in this work, the active learning process monitoring (AL-PM) model is developed. To design a good training set, Gaussian process (GP) models are first used to construct the relationships between the score variables of the latent structure model and the designable process variables because the GP model is capable of providing the accurate predictive mean and variance. The variance can quantify its prediction uncertainty. Second, the uncertainty index is presented and utilized to adequately explore for which regions the new data samples should be used to enhance the quality of the monitoring model. The proposed AL-PM model can be applied to any types of latent structure-based monitoring models. Its effectiveness and promising results have been demonstrated by its applications to a numerical example and a penicillin benchmark process.