Journal
SINTA 1
TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry
Sinergi
ISSN 2460-1217, 1410-2331
Published 2025-09-05
Publisher Universitas Mercu Buana
Language eng
Abstract
Induction motor winding failure repair takes longer compared to other failures, such as bearing failure. This research introduces a hybrid deep learning framework, TE-LSTM, to predict winding temperatures in induction motors used in oil and gas operations, aiming to address the challenges of accurately forecasting potential winding failures and enabling proactive maintenance strategies. The TE-LSTM model combines a transformer encoder-based architecture with long short-term memory to effectively model intricate temporal relationships and sensor dynamics within the dataset. The study utilized data collected from January 2016 to December 2024 at 1-minute intervals from induction motors equipped with stator winding temperature sensors, where the motors were designed with Class F insulation and had stage 1 and stage 2 alarms set at 257°F and 285°F, respectively. The findings highlight the efficiency and performance of the TE-LSTM model in predicting winding temperatures, which can significantly reduce unplanned downtime and associated costs, thereby optimizing maintenance operations and enhancing the reliability of the motor.
Collaboration network of the authors
23 authors ·
62 co-authorship links
· 9 across different organisations
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Metadata source: OAI-PMH (metadata jurnal) https://publikasi.mercubuana.ac.id/index.php/sinergi/oai, first harvested 10 Nov 2026, last seen 10 Nov 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en-US">TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry</dc:title>
<dc:creator>Supriyono, Joko</dc:creator>
<dc:creator>Mukhlash, Imam</dc:creator>
<dc:creator>Iqbal, Mohammad</dc:creator>
<dc:creator>Asfani, Dimas Anton</dc:creator>
<dc:subject xml:lang="en-US">Induction Motor</dc:subject>
<dc:subject xml:lang="en-US">LSTM</dc:subject>
<dc:subject xml:lang="en-US">Predictive Maintenance</dc:subject>
<dc:subject xml:lang="en-US">Transformer</dc:subject>
<dc:subject xml:lang="en-US">Winding Temperature</dc:subject>
<dc:description xml:lang="en-US">Induction motor winding failure repair takes longer compared to other failures, such as bearing failure. This research introduces a hybrid deep learning framework, TE-LSTM, to predict winding temperatures in induction motors used in oil and gas operations, aiming to address the challenges of accurately forecasting potential winding failures and enabling proactive maintenance strategies. The TE-LSTM model combines a transformer encoder-based architecture with long short-term memory to effectively model intricate temporal relationships and sensor dynamics within the dataset. The study utilized data collected from January 2016 to December 2024 at 1-minute intervals from induction motors equipped with stator winding temperature sensors, where the motors were designed with Class F insulation and had stage 1 and stage 2 alarms set at 257°F and 285°F, respectively. The findings highlight the efficiency and performance of the TE-LSTM model in predicting winding temperatures, which can significantly reduce unplanned downtime and associated costs, thereby optimizing maintenance operations and enhancing the reliability of the motor.</dc:description>
<dc:publisher xml:lang="en-US">Universitas Mercu Buana</dc:publisher>
<dc:date>2025-09-05</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
<dc:format>application/pdf</dc:format>
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<dc:identifier>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/31613</dc:identifier>
<dc:identifier>10.22441/sinergi.2025.3.022</dc:identifier>
<dc:source xml:lang="en-US">SINERGI; Vol. 29 No. 3 (2025); 819-832</dc:source>
<dc:source>2460-1217</dc:source>
<dc:source>1410-2331</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/31613/10075</dc:relation>
<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/31613/12263</dc:relation>
<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/31613/12264</dc:relation>
<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/31613/12265</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2025 SINERGI</dc:rights>
</oai_dc:dc>
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Cite this article
APA 7
Supriyono, J., Mukhlash, I., Iqbal, M., & Asfani, D. A. (2025). TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry. Sinergi, 29(3), 819–832. https://doi.org/10.22441/sinergi.2025.3.022
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IEEE
J. Supriyono, I. Mukhlash, M. Iqbal, and D. A. Asfani, “TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry,” Sinergi, vol. 29, no. 3, pp. 819–832, 2025, doi: 10.22441/sinergi.2025.3.022.
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