Inductive learning of decision trees: Application to fault isolation of an induction motor

Denis Pomorski (1) , Paul-Benoît Perche

1 LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal

This work deals with fault detection and isolation (FDI) of an induction motor. Its supervision cannot be performed on the sole knowledge of analytical redundancy relations : a normal functioning state of the motor and a speed-sensor failure state cannot be distinguished from a behavioral analytical model. A solution is proposed using two inductive learning techniques based on decision tree formalism: C4.5 which is a milestone in top–down induction of decision trees, and BUST which is a solution for the functional separability problem of decision trees.

Mots clés: Fault isolation; Analytical redundancy relations; Inductive learning; Decision tree; Entropy

Domaines: Automatique; Théorie de l'information [cs.IT]; Apprentissage [cs.LG]

Denis Pomorski, Paul-Benoît Perche. Inductive learning of decision trees: Application to fault isolation of an induction motor. Engineering Applications of Artificial Intelligence, 2001, 14, pp.155-166. ⟨10.1016/S0952-1976(00)00078-6⟩. ⟨hal-01509601⟩