Chapter 14 - Optimal state and parameter estimation for fault detection and diagnosis in continuous stirred tank reactor

Abstract

Optimal state estimation is the most important problem for model-based fault detection and diagnosis of nonlinear dynamic systems. In quantitative model-based fault diagnosis, both the states and parameters in the process model are estimated by using accessible measurements of the process. This chapter presents the method of extended Kalman filter (EKF) and various two-level methods for fault detection and diagnosis in nonlinear, time-varying, and stochastic chemical processes. The method of EKF is formulated for combined estimation of states and fault parameters. The two-level methods are formulated using different versions of EKF, recursive least squares, and a reduced-order extended Luenberger observer. These methods are specified for state estimation in the first level, and fault diagnosis via parameter identification in the second level. The performances of these methods are evaluated by applying them to a nonlinear continuous stirred tank reactor with a heat exchanger. The reduced order extended Luenberger observer based method has the advantage of estimating the unmeasured process states in the presence of faults. Among various methods, the method of two-level EKF is found to exhibit better performance with lower computational effort.