Adaptive monitoring statistics with state space model updating based on canonical variate analysis

Abstract

A new multivariate statistical model updating by using a recursive state space model updating based on CVA is proposed. The CVA-based monitoring techniques have been researched to detect and isolate process abnormalities in dynamic processes. Two monitoring indices are defined for fault detection, and a state space model updating procedure is developed by using mean, covariance, and correlation updating based on forgetting factor as well as the recursive Cholesky factor updating. To adjust forgetting factors according to variation of process state, the forgetting factor updating criteria are introduced. The proposed method is applied to benchmark models of a continuous stirred tank reactor with a first order reaction and the Tennessee Eastman process (TEP) under transient and time-varying operating conditions. Through the simulation results, we expect that the proposed approach can be applied to time-varying and dynamic processes under transient state.