TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry - Supriyono, Joko; Mukhlash, Imam; Iqbal, Mohammad; Asfani, Dimas Anton (Universitas Mercu Buana, 2025)
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.