ONLINE FAULT-DETECTION IN UNCERTAIN NONLINEAR-SYSTEMS USING DIAGNOSTIC OBSERVERS - A SURVEY

The paper discusses the principles of model-based fault detection and isolation (FDI) in nonlinear and time-varying uncertain dynamic system s. Such systems are typical for such complex plants as, for example, i n the chemical process industries or in advanced transportation techno logy. For a model-based fault diagnosis in such situations, robust or even adaptive strategies are needed. In this paper the theory of robus t linear observer-based residual generation for FDI is reviewed from a general point of view. The structural equivalence between the parity space approach and observer-based approach is shown in a new simple gr aphical way by showing that the observer-based FDI concept can easily be transformed into an equivalent extended parity space configuration, without claiming, however, equivalence of the underlying design techn iques. The unknown input observer approach known as a most powerful an d comprehensive framework for robust residual generation for FDI in un certain linear systems is extended to classes of nonlinear and time-va rying systems. For such plants an adaptive nonlinear unknown input obs erver scheme is proposed. Finally, appropriate residual evaluation tec hniques are outlined and suggestions are made to increase the robustne ss, for instance by using adaptive thresholds.