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The Transactions of
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The Transactions of the Korean Institute of Electrical Engineers
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Trans. Korean. Inst. Elect. Eng.
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2026-03
(Vol.75 No.3)
10.5370/KIEE.2026.75.3.644
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1
J. Basit, A. Zeb, 2024, Predictive maintenance using deep learning: Enhancing reliability and reducing electrical system downtime, International Journal of Innovations in Science and Technology, Vol. 6, No. 3, pp. 1120-1136
2
H.-M. Park, S.-G. Kim, H.-Y. Kim, 2021, IoT-based wireless communication system for power-equipment monitoring
3
G. Liang, 2025, Power System Fault Maintenance System Based on Deep CNN-LSTM, Procedia Computer Science, Vol. 262, pp. 580-587
4
K. Varalakshmi, J. Kumar, 2025, Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques, Scientific. Rep.
5
Z. Z. Darban, G. I. Webb, S. Pan, C. C. Aggarwal, M. Salehi, 2023, Deep learning for time series anomaly detection: A survey, arXiv preprint
6
S. Schmidl, P. Wenig, T. Papenbrock, 2022, Anomaly detection in time series: A comprehensive evaluation, Proceedings of the VLDB Endowment, Vol. 15, No. 9, pp. 1779-1797
7
J. Nolan, S. Reynolds, 2025, Deep Learning Approaches for Predictive Maintenance in Industrial Systems, ITSI Transactions on Electrical and Electronics Engineering, Vol. 13, No. 1, pp. 20-25
8
W. Li, T. Li, 2025, Comparison of Deep Learning Models for Predictive Maintenance in Industrial Manufacturing Systems Using Sensor Data, Scientific Reports, Vol. 15
9
K. Zarzycki, M. Ławryńczuk, 2021, LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors, Sensors, Vol. 21, No. 16, pp. 5625
10
L. Su, X. Zuo, R. Li, X. Wang, H. Zhao, B. Huang, 2025, A systematic review for transformer-based long-term series forecasting, Artificial Intelligence Review, Vol. 58, pp. 1-29
11
J. Backhus, 2025, Time Series Anomaly Detection Using Signal Processing Techniques With Deep Learning Methods, Applied Sciences, Vol. 15, No. 11
12
T.-H. Ha, H.-G. Lee, D.-K. Kim, J.-H. Bae, J.-I. Lee, S.-W. Kim, 2001, Effects of Human Safety due to Leakage Current by Outdoor Electrical Facility in the Submerged Condition(II), pp. 247-249
13
Y. Kim, K. Kim, 2021, Accelerated computation and tracking of AC optimal power flow solutions using GPUs, arXiv preprint
14
S. H. Park, 2023, This is C#
15
M. F. Pekşen, U. Yurtsever, Y. Uyaroğlu, 2024, Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT, Alexandria Engineering Journal, Vol. 96, pp. 112-123
16
S. S. Shah, T. Daoliang, S. C. Kumar, 2024, RUL forecasting for wind turbine predictive maintenance based on deep learning, Heliyon, Vol. 10, No. 20
17
K. Bharatheedasan, T. Maity, L. A. Kumaraswamidhas, M. Durairaj, 2025, Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model, Alexandria Engineering Journal, Vol. 115, pp. 355-369