Principal component analysis (P monitoring CA) is a classical algorithm for feature extraction and has been widely used in multivariate statistical process. The essence of the PCA algorithm is to extract the correlation between process variables. However

the correlation matrix defined in the traditional PCA algorithm is limited to consider the linear relationship between variables

which cannot be employed to analyze the mutual dependence between two measured variables. With recognition of this lack

a novel mutual information based PCA (MIPCA) method is proposed for process monitoring. Distinct from the traditional PCA

MIPCA defines the relationship between variables by calculating the mutual information

and the original correlation matrix is replaced by the resulting mutual information matrix. The eigenvectors of the mutual information matrix can thus be utilized as the directions of feature extraction. On the basis of MIPCA

a statistical process monitoring model can then be constructed. Finally

the feasibility and effectiveness of the MIPCA-based monitoring method are validated by a well-known chemical process.