Recursive Dynamic Transformed Component Statistical Analysis for Fault Detection in Dynamic Processes

Abstract

This paper considers the problem of dynamic process monitoring. Based on the recently proposed recursive transformed component statistical analysis (RTCSA), its dynamic counterpart recursive dynamic transformed component statistical analysis (RDTCSA) is proposed. With time lag shift technique, the augmented sample covariance matrices are used for eigendecomposition and further data transformation. The obtained dynamic transformed components include dynamic information of measurements, whose statistics are used for process monitoring. The difference between RTCSA and RDTCSA for monitoring time-correlated process data is analyzed, which implies that RDTCSA is more sensitive to dynamic changes. In addition, the detectability of RDTCSA for monitoring time-correlated process data is analyzed in a statistical sense. A numerical simulation and the benchmark Tennessee Eastman process (TEP) both indicate the superior fault detectability of RDTCSA compared with the existing methods. Specifically, RDTCSA can effectively detect fault 15 in TEP with detection rate over 95%.