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References

1 
Eui-Jin Choi, Ji-Hoon Han &Sun-Ki Hong, “Performance Improvement of Transfer Learning-based Motor Fault Diagnosis Using Mix-up Data Considering Data Imbalance,” The transactions of The Korean Institute of Electrical Engineers, vol. 72, no. 2, pp. 255-262, 2023.URL
2 
Wang, Yasi, Hongxun Yao, and Sicheng Zhao, “Auto-encoder based dimensionality reduction,” Neurocomputing 184, pp. 232-242, 2016.URL
3 
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M. &Suganthan, P. N., “Ensemble deep learning: A review,” Engineering Applications of Artificial Intelligence, vol. 115, pp. 105-151, 2022.URL
4 
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K. R. &Samek, W., “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,” PloS one, vol. 10, no. 7, pp. 130-140, 2015.DOI
5 
Nishat Toma, Rafia, and Jong-Myon Kim, “Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms,” Applied Sciences, vol. 10, no. 15, 2020.DOI
6 
Saxena, Ankur, Manoj Chouksey, and Anand Parey, “Effect of mesh stiffness of healthy and cracked gear tooth on modal and frequency response characteristics of geared rotor system,” Mechanism and Machine Theory, vol. 107, pp. 261-273, 2017.DOI
7 
Nivesrangsan, Pornchai, and Dutsadee Jantarajirojkul, “Bearing fault monitoring by comparison with main bearing frequency components using vibration signal,” 2018 5th International Conference on Business and Industrial Research (ICBIR). IEEE, pp. 292-296, 2018.DOI