Combining FDI and AI Approaches within Causal-Model-based Diagnosis

Sylviane Gentil (1) , Jacky Montmain (2) , Christophe Combastel (3)

1 LAG - Laboratoire d'automatique de Grenoble
2 LGI2P - Laboratoire de Génie Informatique et Ingénierie de Production
3 ECS - Equipe Commande des Systèmes

This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both Artificial Intelligence and Control Theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary sub-models. Control Theory, within the framework of Fault Detection and Isolation, provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical sub-models that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced

Mots clés: Causal reasoning; Causal graph; Diagnosis; Fault detection; Fault filtering; Fault isolation; Supervision

Domaines: Automatique; Intelligence artificielle [cs.AI]

Sylviane Gentil, Jacky Montmain, Christophe Combastel. Combining FDI and AI Approaches within Causal-Model-based Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34 (5), pp.2207 - 2221. ⟨10.1109/TSMCB.2004.833335⟩. ⟨hal-00353884⟩