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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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References

1 
S.-Y. Lim, U.-H. Jeong, H.-W. Lim, 2016, Study on failure prediction method of BLDC motor driver, J. Adv. Eng. Technol., Vol. 9, No. 2, pp. 105-109Google Search
2 
H. Bae, S. Kim, G. Vachtsevanos, 2009, Fault detection and diagnosis of winding short in BLDC motors based on fuzzy similarity, Int. J. Fuzzy Logic Intell. Syst., Vol. 9, No. 2, pp. 99-104DOI
3 
T. A. Shifat, J. W. Hur, 2020, An effective stator fault diagnosis framework of BLDC motor based on vibration and current signals, IEEE Access, Vol. 8, pp. 106968-106981DOI
4 
B.-G. Park, K.-J. Lee, R.-Y. Kim, D.-S. Hyun, 2010, Low-cost fault diagnosis algorithm for switch open-damage in BLDC motor drives, J. Power Electron., Vol. 10, No. 6, pp. 702-708DOI
5 
J.-W. Sung, E.-G. Lee, S. Kwak, 2025, AI based capacitor aging diagnosis for DC/DC converters, The Transactions of the Korean Institute of Electrical Engineers, Vol. 74, No. 4, pp. 624-628Google Search
6 
I. Ul Hassan, K. Panduru, J. Walsh, 2024, An in-depth study of vibration sensors for condition monitoring, Sensors, Vol. 24, No. 3DOI
7 
C. R. Soto-Ocampo, J. M. Mera, J. D. Cano-Moreno, J. L. Garcia-Bernardo, 2020, Low-cost, high-frequency, data acquisition system for condition monitoring of rotating machinery through vibration analysis—Case study, Sensors, Vol. 20, No. 12DOI
8 
P. Suawa, T. Meisel, M. Jongmanns, M. Huebner, M. Reichenbach, 2022, Modeling and fault detection of brushless direct current motor by deep learning sensor data fusion, Sensors, Vol. 22, No. 9DOI
9 
Z. Zhao, J. Wu, S. Liu, L. Zhao, 2019, A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion, Sensors, Vol. 19, No. 7DOI
10 
W. Zhang, C. Li, G. Peng, C. Yen, Z. Zhang, 2018, A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals, IEEE Trans. Ind. Electron., Vol. 65, No. 5, pp. 4290-4300DOI
11 
O. Janssens, 2016, Convolutional neural network based fault detection for rotating machinery, J. Sound Vib., Vol. 377, pp. 331-345DOI
12 
S. Zhang, S. Zhang, B. Wang, T. G. Habetler, 2019, Machine learning and deep learning algorithms for bearing fault diagnostics—A comprehensive review, IEEE Access, Vol. 7, pp. 136400-136415Google Search
13 
A. Krizhevsky, I. Sutskever, G. E. Hinton, 2012, ImageNet classification with deep convolutional neural networks, pp. 1097-1105Google Search
14 
D. P. Kingma, J. Ba, 2015, Adam: A method for stochastic optimizationGoogle Search
15 
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, P. T. P. Tang, 2017, On large-batch training for deep learning: Generalization gap and sharp minimaDOI