Robust Model-Based Fault Detection in Dynamic Systems

Abstract

The paper discusses the design of robust fault detection and isolation (FDI) techniques using analytical models for nonlinear and time varying uncertain systems. Such systems are typical in the chemical process industries. The unknown input observer approach known as a most powerful and comprehensive framework for robust residual generation for FDI in uncertain linear systems is extended to classes of nonlinear and time varying systems. For such plants an adaptive nonlinear unknown input observer scheme is proposed. Moreover, it is demonstrated from a new graphical point of view that the observer-based approach can easily be transformed into an equivalent extended parity space configuration without claiming, however, equivalence of the resulting design techniques. Finally, residual evaluation techniques are reviewed and suggestions to increase the robustness are made.