Process-based diagnosis: An approach to understanding novel failures

Abstract

This thesis describes a diagnostic technique for explaining unanticipated modes of failure in continuous-variable systems. Previous approaches in model-based diagnosis have traditionally suffered from either a dependence on explicit fault models or a tendency to produce unintuitive results. This research aims at achieving the explanatory power of explicit fault models, without sacrificing the robustness of consistency-based diagnosis. The unique compositional nature of the process-centered models of Qualitative Process Theory makes the application of model-based diagnostic techniques both non-trivial and rewarding. Rather than relying on explicit fault models, this approach utilizes a general domain theory to model the broken device. Given a sufficiently broad domain theory, symptoms are explained in terms of a transformed physical structure. Generative fault models replace explicit, pre-enumerated fault models, thereby increasing robustness for identifying novel faults. This approach combines the efficiency of the consistency-based approach with the explanatory power of abductive backchaining. Candidates generated using a consistency-based approach are used to focus the abductive search for a structural model of the failed system. An implementation built on a modified ATMS and an incremental qualitative envisioner is tested on a number of examples. The systems examined are taken primarily from the domain of thermodynamics, but also include some simple circuits.