Abstract
In this paper, a diagnosis system, using fuzzy logic, for rotor bar defects in the squirrel cage induction motor is proposed. In the iron and steel industry, induction motors are widely used for various facilities. Recently induction motors have been used in rolling mills. Accordingly the requirement for stabilization in their operations has become very stringent. Various methods are used to diagnose the rotor bar defects of the induction motor, such as the frequency analysis method, the neural network method and so on. However it is difficult to quantitatively determine the degree of deterioration by conventional diagnosis methods. To overcome the difficulty, a new identification method using fuzzy logic is proposed for the diagnosis of rotors in the squirrel cage induction motor. Due to a lot of non-linear elements in the induction motor model, conventional identification methods, such as the least squares method, are not applied to the identification of the induction motor parameters. Our proposed method utilizes the simulator based on the mathematical model and the actual data. The rotor bars resistance in the simulator is modified reflecting the difference between the calculated current and the measured current. After iteration of these modification steps, the rotor bars resistance in the simulator converges to its real value. Using our proposed method, the trend of the rotor deterioration can be managed quantitatively, enabling the appropriate condition-based maintenance of the induction motors.
