In complicated industrial systems

without the first component model and just by use of routine data

factor analysis can model the process operation and take full consideration of general sense of model error

therefore it has a good application prospect.Aimed at the dynamic characteristics in the actual process

the paper proposes a novel method based on dynamic factor analysis (DFA) to model the process data.In DFA

the sample matrix is extended based on the auto-regressive (AR) model

so it can well extract the information of auto-correlation and cross-correlation among process variables.In order to evaluate the operational condition of the process

the paper constructs several statistics as the monitoring indices to measure the features and errors

respectively.The application in the Tennessee-Eastman (TE) process shows the superiority of the DFA-based method.