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<p>Induction motors play a critical role in industrial operations and electric vehicles, yet their reliability is often compromised by stator winding inter-turn short-circuit (ITSC) faults. To mitigate costly downtimes, this study investigates machine learning-based fault detection methods, comparing Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). A benchmark dataset comprising stator current signals, leakage flux, and varying load conditions was employed to train and evaluate the models. While classification across seven fault categories remained challenging, the proposed framework achieved outstanding results in binary classification (healthy vs. faulty). In particular, the LSTM model attained a near-perfect accuracy of 100%, and the PINN model achieved 99.32%, both surpassing the baseline ANN performance of 99.01%. These findings highlight the effectiveness of temporal modeling with LSTM and the added value of incorporating motor physics into PINNs, especially in scenarios with limited data. The results confirm that advanced machine learning models can significantly improve early fault detection, enabling more reliable predictive maintenance and reduced operational costs in industrial systems.</p>

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Predicting stator winding short-circuit faults in induction motors using machine learning: a comparative study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Tayyaba Aasar,
  • Kiran Sultan,
  • Adnan Umer Khan,
  • Silvester Czanner,
  • Ayesha Abbasi,
  • Aasar Ahmad

摘要

Induction motors play a critical role in industrial operations and electric vehicles, yet their reliability is often compromised by stator winding inter-turn short-circuit (ITSC) faults. To mitigate costly downtimes, this study investigates machine learning-based fault detection methods, comparing Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). A benchmark dataset comprising stator current signals, leakage flux, and varying load conditions was employed to train and evaluate the models. While classification across seven fault categories remained challenging, the proposed framework achieved outstanding results in binary classification (healthy vs. faulty). In particular, the LSTM model attained a near-perfect accuracy of 100%, and the PINN model achieved 99.32%, both surpassing the baseline ANN performance of 99.01%. These findings highlight the effectiveness of temporal modeling with LSTM and the added value of incorporating motor physics into PINNs, especially in scenarios with limited data. The results confirm that advanced machine learning models can significantly improve early fault detection, enabling more reliable predictive maintenance and reduced operational costs in industrial systems.