The use of features selection and nearest neighbors rule for faults diagnostic in induction motors

This paper deals with the diagnosis of induction motors by pattern recognition methods. The objective is to use existing theories to improve the diagnosis procedures in electrical engineering. First of all, a single signature is determined to monitor several different operating modes. For this purpose, features are extracted from the combination of the stator currents and voltages. Then, the sequential backward algorithm is applied in order to select the most relevant features. The classification is performed by the k-nearest neighbors rule with reject options. The methodology is applied on a 5.5 kW motor in normal conditions, then with stator and rotor faults. The experimental results prove the efficiency of pattern recognition methods in condition monitoring of electrical machines.