Abstract
Statistical process control （SPC） is a methodology for monitoring sequential processes to make sure of stable and proper performance in process quality. In particular, control charts are a quantitative management tool utilized in SPC and basically monitor a process condition utilizing quality characteristics with stochastic variability. The major part of control chart research has been developed around mathematical statistics until now. Nowadays various theories and technologies have been applied to control charts such as information theory and machine learning. This manuscript introduces a major part of my research briefly first, and then my research on control charting methodology based on such as information theory, statistical science, and Bayesian theory. The latest research topics are focusing on change point detection （CPD）, information visualization, and process stability. At last, this manuscript is concluded through the prospect of my future research.
