Prediction of stator slot size variation in industrial drives using GMR sensor signal and regression technique
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Krishnan Selvaraj
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Senthil Rama Rajamarthandan
Abstract
Stator Slot Size Variation (SSSV) in the induction motor occurs due to magnetic stress,a force caused by stretching the magnetic flux lines across the surface of a conducting material. This is a serious issue that affects the performance of induction motors. The amount of magnetic stress produced is proportional to the average SSSV measured in the stator. Induction motor faults such as stator winding faults, rotor winding faults, voltage unbalance, phase reversal and overload are the causes of high magnetic stress. Variation in the stator slot causes greater leakage flux, which subsequently decreases the performance, and the harmonic level is increased in the induction motor. In this proposed work, multimodal sensors are used to acquire the flux, vibration, temperature and current from the induction motor. The multimodal sensor signals obtained from the induction motor is analysed using Hilbert-Huang Transform (HHT) to calculate Energy Band Values (EBV).The Microscopic Camera Images (MCI) values and the calculated EBV are used to predict the SSSV using Decision Tree Regression (DTR) algorithm. According to experimental findings, a stator slot that deviates more than 0.1 % from its typical size causes a large magnetic stress. Early prediction of high magnetic stress and faults in an induction motor may lead to avoid unnecessary motor faults by measuring SSSV. The efficiency of the proposed method for predicting SSSV in induction is about 95.8 %.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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