ANN Based Double Stator Asynchronous Machine Diagnosis Taking Torque Change into Account

In this work the strategy of the artificial intelligence (neural networks) is used to detect and localize the defects of the double stator asynchronous machine. In fact, several neural networks have been applied to the detection of defects. Then, we used a selector which allows activating only one network at a time. In this case, the selected network detects only defects corresponding to the torque developed by asynchronous machine. Finally, the simulation results were presented to show the effectiveness of artificial neural networks for automatic fault diagnosis.