In this article a fault detection scheme for different percentage of stator winding short circuit is presented for three phase induction motors. In the examined case, the induction motor in the faulty and healthy case has been transformed in the two phase (q−d) model. The model has been identified by the utilization of a Least Squares Set Membership Identification (SMI) algorithm, where additional to the identified parameters, confidence intervals can be also calculated, based on a priori knowledge for the corrupting measurement noise. The identified confidence intervals in an μ–dimensional space can be represented as hyper–ellipsoids having as a center the identified parameters’ vector. The novelty of this article stems from the proposal of a fast and geometrical based scheme, which relies on the calculation of the distance among centers of hyper–ellipsoids and the corresponding intersection in each iteration of the identification procedure. Detailed analysis of the proposed fault detection strategy, as also extended simulation results are being presented that prove the efficiency of the suggested scheme.