Performance monitoring of a CSTR plant using asynchronous data fusion based on Extended Kalman Filter

Abstract

This paper presents the state estimation problem for a nonlinear industrial plant using asynchronous measurements. A novel approach based on Extended Kalman Filter (EKF) is proposed to deal with estimation problem of sensors having different time delays and different sampling rates. The main idea of the suggested method is to update state and covariance without filter recalculation. The performance of the proposed method will be investigated through a simulation case study conducted on a continues stirred tank reactor as an industrial nonlinear benchmark. The simulation results demonstrate the superiority of the proposed method in comparison with a previously reported approach [15].