Open-circuit fault diagnosis in three-phase induction motor using model-based technique

The presence of an open-circuit fault subjects a three-phase induction motor to severely unbalanced voltages that may damage the stator windings consecutively causing total shutdown of systems. Unplanned downtime is very costly. Therefore, fault diagnosis is essential for making a predictive plan for maintenance and saving the required time and cost. This paper presents a model-based diagnosis technique for diagnosing an open-circuit fault in any phase of a three-phase induction motor. The proposed strategy requires only current signals from the faulty machine to compare them with the healthy currents from an induction motor model. Then the errors of comparison are used as an objective function for a genetic algorithm that estimates the parameters of a healthy model, which they employed to identify and localize the fault. The simulation results illustrate the behaviours of basic parameters (stator and rotor resistances, self-inductances, and mutual inductance) and the number of stator winding turn parameters with respect to the location of an open-circuit fault. The results confirm that the number of stator winding turns are the useful parameters and can be utilized as an identifier for an open-circuit fault. The originality of this work is in extracting fault diagnosis features from the variations of the number of stator winding turns

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

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