Prediction of Rotor Slot Size Variation Through Vibration Signal of Three Phase Induction Motor Using Machine Learning

Purpose Induction Motor (IM) is used in various industrial application such as drilling, rolling, paper mills, lathe machine and milling. The faults in IM are avoided through continuous monitoring and control, during running conditions. This research paper presents an innovative method for detecting motor faults such as unbalanced voltage, phase reversal, frequent starting, abnormal speed, low voltage, phase loss, turn-to-turn short circuit, shaft bend and predicting Rotor Slot Size Variation (RSSV) during motor faults. MethodsThe RSSV and rotor faults of the IM are due to magnetic and Thermal Stress (TS). Magnetic and thermal stresses are measured through GMR, and temperature sensor. The vibration sensor measures abnormal motor vibration during rotor faults. In this proposed method, Least Square Support Vector Machine (LS-SVM) and Symmetrized Dot Pattern (SDP) are used to match the sensor signals of normal and abnormal motors and identify motor faults.ResultsThe signals collected from IM are changed into 2D images using the concept of SDP, which shows the difference among various induced faults through dot patterns of signal data points. Energy band values are obtained from the GMR sensor signal using Scale Invariant Feature Transform (SIFT) and Microscopic Camera Image Values (MCIV) are provided to the Multiple Linear Regression (MLR) for predicting the RSSV.Conclusion The prediction accuracy of rotor slot size through the proposed technique is 94.8%. From the experimental results, if the RSSV is more than 2% of the standard size, leads to heavy damage to the IM.