External analysis and moving window LoOP based monitoring for a class of multi-mode timevarying process

Abstract

Moving window local outlier probability U+0028 MWLoOP U+0029 is an outlier detecting method which was proposed for monitoring time-varying industrial processes; however, for the practical industrial processes, besides the timevarying characters caused by deactivation of catalyst, measuring instrument drifting and so on, the operation mode is often switched as the adjusting of the feedstock, changes in market demands and so on. Although the MWLoOP algorithm can deal with the time-varying process data, the multi-mode process data will lead to a mass of false alarm. To solve this problem, an external analysis moving window local outlier probability U+0028 EA-MWLoOP U+0029 algorithm is proposed in this study. The external analysis is employed to eliminate the influence of operation mode change on the process data, then the MWLoOP method can deal with complex distribution time-varying data, and give an outlier probability. Finally, the corresponding statistic and control limit are constructed to detect the process fault. In addition, while the monitoring model is being updated, the control limit is not necessary to update. The performance of this method is evaluated through a case study of a non-isothermal continuous stirred tank reactor U+0028 CSTR U+0029.