Fault detection of an induction motor by set-membership filtering and Kalman filtering

Two approaches are presented in this paper to estimate the state of an induction motor and detect faults: a geometric approach, assuming only that the perturbations belong to known bounded sets with no hypothesis on their distributions inside these sets, and a stochastic approach by Kalman filtering. Recursive and explicit algorithms are presented and illustrated by real data of an induction motor that has been designed to have some more and less important faults.