Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty
This work addresses fault diagnosis in systems with qualitative uncertainty, but it appears incremental as it builds on existing possibilistic methods without major breakthroughs.
The paper tackles fault isolation using incomplete models by focusing on anomaly propagation and abductive explanations, with a realistic example demonstrating the approach's benefits.
An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.