Dissimilarity of Process Data for Statistical Process Monitoring

For monitoring chemical processes, multivariate statistical process control (MSPC) has been widely used. In the present work, a new process monitoring method is proposed. The proposed method utilizes a change in distribution of process data, since the distribution reflects the corresponding operating condition. In order to quantitatively evaluate the difference between two data sets, the dissimilarity index is defined. The proposed method and the conventional SPC methods are applied to monitoring problems of the Tennessee Eastman process. The results have clearly shown that the monitoring performance of the proposed method is considerably better than that of the conventional methods.