MOTOR CURRENT SIGNATURE ANALYSIS UNDER FAULTS

Abstract

In this work, Motor Current Signature Analysis (MCSA) has been done for stator and rotor faults of an Induction motor using Balanced Sinusoidal supply and Non-Sinusoidal Balanced supply (Voltage Source Inverter). Rotor broken faults diagnosis using ANN has been employed. MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. Due to faults, there exists a frequency components induced in the stator current. When a fault is present, the frequency spectrum of the line current becomes different from that of healthy motor. MCSA does not require any additional sensors and makes use of already available sensors employed for protection. Hence MCSA is a non-invasive, on- line monitoring technique for the diagnosis of faults in induction motors. Induction Motor has been modeled under faults for MCSA using MATLAB software. The current spectrum was obtained using signal processing techniques. For obtaining adjustable speed, induction motors in industry are fed from voltage source inverters (VSI). Harmonics are introduced in supply due to inverters, which make it a difficult task to distinguish the faulty spectrum components from healthy components. Hence, stator and rotor faults of VSI fed induction motor are also diagnosed using MCSA in present work. Neural Network is one of the accurate and reliable soft computing techniques. Based on the rotor harmonics due to rotor broken faults and slip, a neural network method to diagnose the rotor broken bar faults of an inductor motor is presented.