Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

Abstract

The widespread use of three-phase cage induction motors in so many critical industrial, commercial and domestic applications means that there is a real need to develop online diagnostic systems to monitor the state of the machine during operation. This paper presents a hybrid diagnostic system that combines a model-based strategy with a fuzzy logic classifier to identify abnormal motor states due to single-phasing or inter-turn stator winding faults. Only voltage and current measurements are required to extract the fault symptoms, which are represented as model parameters variations in an equivalent virtual healthy motor, negating the need to use complex models of faulty machines. A trust-region method is used to estimate the machine model parameters, with the final decision on the type, location and extent of the fault being made by the fuzzy logic classifier. The proposed diagnostic system was experimentally verified using a 1.0 hp three-phase test induction motor. Results show that the proposal method can efficiently diagnose single phasing and inter-turn stator winding faults even when operating with unbalanced supply voltages and in the presence of significant levels of measurement noise.