Inductive learning of decision trees: application to fault isolation of an induction motor
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 topdown induction of decision trees, and BUST which is a solution for the functional separability problem of decision trees.
inductive learning decision tree fault isolation supervision analytical redundancy relations entropy