For the nonlinear and dynamic characteristics of chemical processes

a process monitoring method based on dynamic one-class random forest (DOCRF) is proposed. The sparsity of the process data under normal operating conditions is analyzed. Then

outliers are produced according to the inverse distribution of the normal data. Canonical variate analysis is used to analyze the correlation of normal data

and to project the normal data and outliers into canonical variate space. One-class random forest is trained by using the data in canonical variate space. The monitoring statistic is established according to the similarity between the test sample and the normal data in one-class random forest for fault detection. Simulation results on Tennessee Eastman process showed that the proposed DOCRF method was better than one-class support vector machine overall.