Prediction of Rotor Slot Size Variations in Induction Motor Using Polynomial Chirplet Transform and Regression Algorithms

Abstract

For constant monitoring of rotor slot in induction motor, average rotor slot variation (ARSV) prediction is proposed. The rotor slots expand due to thermal stress and high intensity magnetic flux. The magnetic flux with high intensity is created in rotor lamination sheet, because of stretching and curving magnetic flux. The surface of rotor slot exhibits thermal stress due to over current which in turn generates heat and transfers the heat into the rotor lamination surface. The magnetic stress-based rotor slot size variation is never monitored or measured before. In the proposed ARSV method, multimodal sensor signals such as Giant magnetoresistance, temperature, current, vibration and voltage acquire magnetic stress and temperature stress of rotor. The acquired signals are analyzed through polynomial chirplet transform to obtain energy band values of signal. The energy band values and microscopic camera image-based rotor slot variation values are used for ARSV prediction. The prediction of ARSV is computed using polynomial regression (PR) method. From experimental results, it is observed that ARSV is greater than 5% from the normal size which leads to extreme damage to the motor through vibration, sparking and harmonics. The prediction accuracy of ARSV is about 94.6% when compared to manual measurement of slots in rotor. Moreover, the prediction of ARSV avoids the major faults, namely eccentricity, overload, under voltage, unbalance voltage, induced rotor slot, rotor crack and rotor burn.