Quality-Related Time-Varying Process Monitoring

Data-driven process monitoring techniques (Ge in Chemom Intell Lab Syst 171:16–25, 2017; Ge and Song in Multivariate statistical process control: process monitoring methods and applications. Springer, 2013; Yin et al. in IEEE Trans Industr Electron 62:657–667, 2015; Ding in J Process Control 24:431–449, 2014) are very popular in industrial process safety detection due to their easy implementation and low requirements for the underlying model. The PCA (Jolliffe in Principal component analysis, Springer, New York, NY, USA, 1986) and PLS (Wold et al. in Pattern regression finding and using regularities in multivariate data, U. K., Applied Science, London, 1983) are two typical MSPC methods for monitoring process security by projecting measurements into a low-dimensional space and constructing Q statistics and T2 statistics (Qin in Annual Review of Control 36:220–234, 2012). When an abnormal variable occurs in an industrial process, there are two possible scenarios. One is that the fault directly affects the output variables, which needs to be alarmed in time.