Title: Transparent sequential learning for nonparametric sequential process monitoring
Authors: Peihua Qiu - University of Florida (United States) [presenting]
Abstract: Machine learning methods have been widely used in process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised machine learning methods would have difficulties because a required training dataset containing both in-control and out-of-control (OC) process observations is rarely available in SPC applications. In addition, many machine learning methods work like black boxes, and it is difficult to interpret their learning mechanisms. In the SPC literature, there have been some existing discussions on how to handle the lack of OC observations in the training data, using the one-class classification, artificial contrast, and some other ideas. However, these approaches have their own limitations. We present a recent method that extends the self-starting process monitoring idea to a general learning framework for monitoring processes with serially correlated data. Under the new framework, process characteristics to learn are well specified in advance, and process learning is sequential in the sense that the learned process characteristics keep being updated during process monitoring. The learned process characteristics are then incorporated into a control chart for detecting process distributional shifts based on all available data by the current observation time. Numerical studies show that process monitoring based on the new learning framework is reliable and effective for SPC.