Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection

Abstract

The development of process monitoring and control methods is important to maintaining product quality in chemical plants safely and effectively. Therefore, multivariate statistical process control (MSPC) methods have been developed, but traditional MSPC methods cannot detect faults relating to process variables that are difficult to measure online. In this work, a new MSPC method including soft sensor prediction is proposed to solve this problem. Soft sensors predict values of difficult-to-measure variables that are used as input variables of fault detection models. The proposed method enables the real-time control of processes using difficult-to-measure variables. The fault detection performance of the proposed method is demonstrated and compared with that of traditional MSPC methods using the Tennessee Eastman process and real industrial process data sets. The results show that the proposed method can achieve more accurate and earlier fault detection than traditional MSPC methods.