Fuzzy model based on-line stator winding turn fault detection for induction motors

A fuzzy model based on-line turn fault detection approach for induction motors is presented in this paper. Two T-S fuzzy models are employed to detect turn fault, one is used to estimate the fault severity, the other is used to determine the exact number of fault turns. During fuzzy modeling, a fuzzy clustering algorithm based on similarity assessing is proposed to determine the optimal structure of the model and real-coded genetic algorithm (GA) is adopted to online optimize model parameters. All these techniques make the fuzzy model compact and accurate. Based on it, Experiments are carried out on a special rewound laboratory induction motor, the results show T-S fuzzy model based diagnosis model determines the shorted turns exactly, and is more effective than the forward neural network based diagnosis model under the condition of detecting a slowly developing turn fault.