Principal component analysis (PCA)

a classical algorithm for feature extraction

has been widely used in multivariate process monitoring. Conventional PCA selects those principal components with larger variance in order to maintain more information of modeling samples. However

when process information is changed

principal components with smaller variance may exhibit more obvious transformation

which means they are more informative and more beneficial for fault detection. Hence

a new process monitoring method was proposed on a basis of informative principal component subspace reconstruction (Info-PCA). Info-PCA calculated change rates of cumulative T2 of process data in different directions of principal components and reconstructed a principal component subspace by selecting those components with larger change rates. Then

a statistical process monitoring model was built. Finally

feasibility and validity of the Info-PCA monitoring method were demonstrated by a case study of a chemical process.