Neural network for the diagnosis of rotor broken faults of induction motors using MCSA

Induction motors are workhorses of industry. Hence, fault diagnosis of an induction motor is vital in every industry for reduction of maintenance cost, safety, increase of production etc. Motor Current Signature Analysis is a non invasive online monitoring technique for the fault diagnosis of induction motors. In this paper, an Induction motor has been modeled using coupled circuit modeling of induction motor. This model is successfully used to simulate the induction motors at healthy and faulty cases. This model gives the complete information of stator current required for MCSA. Rotor broken bars results rotor asymmetry, which induces a frequency components around fundamental in stator current. Neural network is one of the reliable soft computing techniques. Based on the rotor harmonics and slip, a neural network method to diagnose the rotor broken faults of an induction motor is presented.