Decision procedures for fault detection and isolation derived from knowledge and data

Alexandre Evsukoff (1) , Philippe Weber (1) , Sylviane Gentil (1)

1 LAG - Laboratoire d'automatique de Grenoble

This work presents a unified approach to derive decision procedures for model based fault detection and isolation (FDI) either from knowledge or from experiments. In the knowledge-based approach, fuzzy rule weights are defined directly from model structure. In the supervised learning approach, the decision procedure is derived from a data set. The symbolic to numeric integration provided by fuzzy sets in the proposed framework allows integrating symbolic symptoms into the decision procedure. The proposed method is applied to the FDI of a winding machine.

Mots clés: Fault Detection and Isolation; Fuzzy Systems; Knowledge Based Systems; Supervised Learning

Domaines: Automatique / Robotique

Alexandre Evsukoff, Philippe Weber, Sylviane Gentil. Decision procedures for fault detection and isolation derived from knowledge and data. European Control Conference, ECC99, 1999, Karlsruhe, Germany. pp.1-6. ⟨hal-00092874⟩