Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA

Abstract

As manufacturing quality has become a decisive factor in competing in a global market, statistical quality techniques such as statistical process control (SPC) are becoming very popular in industries. With advances in sensing and data capture technology, large volumes of data are being routinely collected in automatic controlled processes. There is a growing need for SPC monitoring and diagnosis in these environments, but an effective implementing scheme is still lacking. This research provides an integrated approach to simultaneously monitor and diagnose an automatic controlled process by using dynamic principal component analysis (DPCA) and minimax distance classifier. Through a step-by-step implementation procedure, the proposed scheme is expected to have an impact on many manufacturing industries with automatic process control (APC) or engineering process control (EPC).