Abstract
In this chapter, the artificial intelligence approach to model-based diagnosis is introduced. First, we present the main ideas of the Consistency-Based Diagnosis (CBD) methodology (the no-function-in-structure principle, the use of models of correct behavior, and the requirement of local propagation in the models), together with its logical formalization provided by Reiter’s work. In CBD, concepts such as (minimal) conflicts and (minimal) diagnoses play a major role because they allow to characterize and to compute the whole set of diagnosis in an automated way. Second, we introduce the General Diagnosis Engine (GDE) which is the de facto computational paradigm for CBD, and we explain how it works. Finally, to increase the discriminative power in CBD results due to using only correct behavior models, we introduce the concept of fault models and explain how CBD can be extended to with predictive fault models, while retaining the essential no exoneration assumption.
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Notes
- 1.
Sometimes, the component interconnections are also called the topology of the system.
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Alonso-González, C.J., Pulido, B. (2019). Model-Based Diagnosis by the Artificial Intelligence Community: The DX Approach. In: Escobet, T., Bregon, A., Pulido, B., Puig, V. (eds) Fault Diagnosis of Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-17728-7_5
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