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

Aasar, Tayyaba
; 
Sultan, Kiran
; 
Umer Khan, Adnan
; 
Czanner, Silvester
; 
Abbasi, Ayesha
; 
Ahmad, Aasar
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Affiliation
International Islamic University Islamabad; University of Chester
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Publication Date
2026-02-17
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Computer Sciences
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Abstract
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.
Citation
Aasar, T., Sultan, K., Khan, A. U., Czanner, S., Abbasi, A., & Ahmad, A. (2026). Predicting stator winding short-circuit faults in induction motors using machine learning: a comparative study. Discover Computing, 29(1), 103. https://doi.org/10.1007/s10791-026-09984-0
Publisher
Springer Nature
Journal
Discover Computing
Research Unit
URI
https://chesterrep.openrepository.com/handle/10034/629883
DOI
10.1007/s10791-026-09984-0
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PubMed Central ID
Type
Article
Language
en
Description
The version of record of this article, first published in [Discover Computing], is available online at Publisher’s website: http://dx.doi.org/10.1007/s10791-026-09984-0
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ISSN
2948-2984
EISSN
2948-2992
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unfunded
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https://link.springer.com/article/10.1007/s10791-026-09984-0
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