For constant monitoring of rotor slot in Induction Motor (IM), 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 (SC) magnetic flux. The surface of rotor slot exhibits thermal stress due to over current, which in turn generates heat in rotor transferred to rotor lamination surface. The magnetic stress based rotor slot size variation is never monitored or measured. In the proposed ARSV method, multimodal sensor signals such as Giant magnetoresistance (GMR), Temperature, Current, Vibration and Voltage acquires magnetic stress & temperature stress of rotor. Acquired signals are analyzed through Polynomial Chirplet Transform (PCT) 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, ARSV greater than 5 % leads to extreme damage to the motor through vibration, sparking and harmonics. The accuracy of prediction for 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 and rotor crack rotor burn.
Predictive maintenance is required for Induction Motor (IM) to avoid sudden breakdown. In this paper, multimodal sensor data are used for prediction of the rotor slot width variation during runtime of IM. Moreover sensor data are visualized through visual image obtained using Scale Invariant Feature Transform (SIFT) and matching dictionary for various faults in motor such as overload, high speed, Broken Rotor bar (BRB), and unbalance magnetic pull. The multimodal sensor signals data acquired from different parts of three phase induction motor are analysed using various transforms such as Over Complete Rational Dilation Wavelet Transform (ORaDWT), Tuneable Q Wavelet Transform (TQWT), Polynomial Chirplet Transform (PCT) and Dyadic Wavelet Transform (Dyadic WT) for various fault conditions and rotor slot width mentioned during runtime condition of motor. The rotor slot width is predicted using Multiple Linear Regression (MLR), Polynomial Regression (PR), Logistic Regression (LR) and Soft-max Regression (SR) methods through the mean value and energy band of the acquired sensor signal. The Dyadic WT and SR perform better for rotor width prediction. The proposed method such as Dyadic WT and SR provides the prediction accuracy of about 95.2%. From experimental results the rotor slot width expansion more than 2% needs immediate attention to avoid breakdown of motor.
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