Jurnal
SINTA 1
TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry
Sinergi
ISSN 2460-1217, 1410-2331
Terbit 2025-09-05
Penerbit Universitas Mercu Buana
Bahasa eng
Abstrak
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.
Jaringan kolaborasi para penulis
23 penulis ·
62 tautan kepenulisan bersama
· 9 lintas organisasi
Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.
Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.
Sumber metadata: OAI-PMH (metadata jurnal) https://publikasi.mercubuana.ac.id/index.php/sinergi/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<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>
<dc:format>application/vnd.openxmlformats-officedocument.wordprocessingml.document</dc:format>
<dc:format>application/vnd.openxmlformats-officedocument.wordprocessingml.document</dc:format>
<dc:format>application/vnd.openxmlformats-officedocument.wordprocessingml.document</dc:format>
<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>
Sitasi dan pengindeksan
OpenAlex
Terindeks
lewat DOI
2 sitasi
Crossref
Belum diperiksa
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
OpenAlex juga mencatat karya ini di: crossref.
Sitasi artikel ini
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
Salin
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.
Salin
Dibuat dari metadata jurnal; periksa nama penulis sebelum dipakai.