A fault monitoring method based on distributed independent component analysis-principal component analysis (ICA-PCA) model is proposed

which is suitable for complex industrial process that cannot be divided into several sub-blocks through an automatic way and has non-Gaussian information. Firstly

an initial PCA decomposition is carried out upon the variables of the whole process. By constructing sub-blocks through different directions of PCA principal components

the original feature space can be automatically divided into several sub-feature spaces. In addition

a two step extractions of the ICA-PCA information are carried on upon all sub-blocks in order to extract both Gaussian and non-Gaussian information

establishing the new statistics and their statistic limits. Finally

the simulation of TE process shows that the proposed fault detection model is efficient and feasible.