Image processing to a neuro-fuzzy classifier for detection and diagnosis of induction motor stator fault

in this paper a new algorithm for the detection of three-phase induction motor stator fault is presented. This diagnostic technique is based on the identification of a specified current pattern obtained from the transformation of the three-phase stator currents to an equivalent two-phase system. This new algorithm proposes a pattern recognition method to identify induction motor stator faults. The proposed neuro-fuzzy approach is based on the index of compactness, and also indicates the extension of the stator fault. This feature is obtained throw the image processing and used as an input in the neuro-fuzzy classifier. Using the neuro-fuzzy strategy, a better linguistic knowledge and an accurate learning capability underlying the motor faults detection and diagnosis process can be achieved. Simulation and experimental results are presented in order to verify the effectiveness of the proposed method.