Detecting and Isolating Faults In Diagnostic Models of Complex Systems

Abstract: Model-based Fault Detection and Isolation (FDI) systems rely on a model of a real-world system and on some observations obtained from sensor readings to determine what faults are present in that same system at a given instant in time. In this framework, it is sometimes assumed that the models used are a true representation of the artifact under study. Unfortunately, in real-world applications this is not always the case and errors in models seldom entail false diagnoses with huge economical consequences. Let us call the problem of detecting and identifying faults in models a problem of meta-diagnosis; an unsolved issue in FDI and a very difficult problem to address especially in the case of complex systems. In this paper, we contribute by providing FDI with a method of meta-diagnosis making use of the strict connection between the FDI and DX worlds; and illustrate such contribution with an example focusing on a DC motor. Finally, we generalize the idea of meta-diagnosis for detecting and identifying errors in observations and algorithms