Fault Diagnosis for Linear Systems

This chapter outlines the basic principles and most important approaches of modelbased fault detection and isolation (FDI) and, to a certain degree, fault diagnosis, using linear models. Both the parity space approach and the concept of observer-based residual generation are described in input-output format. The well established parameter estimation approach to fault analysis is briefly described in terms of using least squares estimates. It is shown how fault isolation (and robustness with respect to unknown inputs, i.e., modeling uncertainties and unmodeled disturbances) can be achieved with the fault detection filter and by decoupling in the frequency domain on the basis of observer-based residual generation. Full and approximate decoupling techniques are addressed for both structured and unstructured modeling uncertainties. It is further shown how structured residuals can be generated for sensor and actuator fault detection using the dedicated observer scheme, DOS, and the generalized observer scheme, GOS. As far as residual evaluation is concerned, we focus our consideration on the threshold test with adaptive thresholds and briefly explain how to find the threshold selector.