Two-level methods for incipient fault diagnosis in nonlinear chemical processes

Abstract

Various two-level methods are formulated by using different forms of extended Kalman filter, recursive least squares and a reduced-order Luenberger observer for fault detection and diagnosis in nonlinear, time-varying and stochastic chemical processes. These methods are specified for state estimation in the first level and fault diagnosis via parameter identification in the second level. The performances of the proposed methods are evaluated by applying them to a nonlinear CSTR with a heat exchanger and a nonlinear batch beer fermentation with closed-loop control. Dynamic simulation results demonstrate that these two-level methods can be applied for process fault detection and diagnosis to transient time domain systems with closed-loop control in addition to the steady-state systems of normal operation. Among these methods, the two-level extended Kalman filter has exhibited better performance. Therefore, the method of the two-level extended Kalman filter is recommended for incipient fault diagnosis.