Induction motor’s rotor slot variation measurement using logistic regression

Abstract

Rotor slots in induction motor expand due to thermal imbalance and create magnetic stress. Magnetic stress is a force that develops on the laminated surface of the rotor due to the curving or stretching magnetic flux. Traditional motor fault detection methods never measure magnetic stress on the rotor; a significant problem frequently arises in the motor. Magnetic stress is proportional to slot size variations in the rotor. High slot size variations on the laminated surface of the rotor lead to more curving and stretching magnetic flux and damage the rotor and stator, reducing their efficiency and induce harmonics. In this paper, the Average rotor Slot Size Variation (ASSV) in the rotor is predicted during the running condition of the motor through logistic regression. Logistic regression predicts ASSV by multimodal sensor signal sub-band energy values and measures rotor slot sizes from microscope images. Multimodal sensor signal is obtained from various sensors, such as vibration, temperature, current and Giant Magneto Resistance (GMR). Signal sub-band energy is obtained from Over complete Rational-Dilation Wavelet Transform (ORaDWT). From experimental results, ASSV is more than 75% from standard size, damaging the rotor and stator. The accuracy of ASSV prediction is about 92%.