Meta-Diagnosis in FDI: Reasoning About False Analytical Redundancy Relations

Abstract

Fault Detection and Isolation (FDI) analytical-redundancy-based 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 time. In this framework, it is sometimes assumed that the models used are a true representation of the artefact under study. Unfortunately, in real-world applications this is not always the case and errors in models may entail false diagnoses with huge economic consequences. Call the problem of detecting and identifying faults in models a problem of meta-diagnosis; an unsolved issue in the FDI community and a very difficult problem to address especially in the case of complex systems. In this paper, we contribute by providing this community with a method of meta-diagnosis making use of the link between the FDI analytical redundancy approach and the DX consistency-based logical approach; and illustrate such contribution with a DC motor example. Finally, the meta-diagnosis is generalised for detecting and identifying errors in observations and algorithms.