As a classical algorithm for feature extraction

principal component analysis (PCA) has been widely used in multivariate statistical process monitoring. However

PCA and its various improved methods extracted from original data only one layer of features but no deep layer features. The development of deep learning technology in computer field indicates that deep network structure is beneficial to extraction of data features. Therefore

principal component analysis network (PCANet)

a deep learning network structure

was introduced into fault detection and combined with multivariate statistical process monitoring method to enhance fault detection efficiency. Under framework of PCANet

state space model was added to network structure as dynamic layer to solve dynamic issue of industrial process data. In addition

the output layer was redesigned to use fault detection as target function. Finally

method feasibility and validity for fault detection were verified by simulated testing on the Tennessee Eastman (TE) process.