On-line incipient fault detection of induction motors using artificial neural networks

This paper develops a novel approach for online detection of incipient faults in single phase squirrel-cage induction motors through the use of artificial neural nets (ANNs). Two of the most common types of incipient faults are indicated: stator winding fault and bearing wear under constant load torque conditions. From the description of motor dynamics, the nonlinear relation of motor parameters also indicated. Simulation results show that the application of ANN to fault diagnosis of motors is reliable