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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 
J. H. Han, H. S. Kim, D. J. Choi, S. K. Hong, Performance Estimation of Motor Fault Diagnosis Using Deep Learning Algorithm by Data Characteristics, KIEE Autumn conference 2018.Google Search
2 
K. S. Gaeid, H. W. Ping, M. Khalid, A. L. Salih, 2011, Fault diagnosis of induction motor using MCSA and FFT, Elect. Electron. Eng., Vol. 1, No. 2, pp. 85-92DOI
3 
N. Sikder, K. Bhakta, A. A. Nahid, M. M. M. Islam, 2019, Fault Diagnosis of Motor Bearing Using Ensemble Learning Algorithm with FFT-based Preprocessing, 2019 International Conference on Robotics, Vol. electrical and signal processing techniques (icrest), pp. 564-569DOI
4 
D. Zhen, Z. L. Wang, H. Y. Li, H. Zhang, J. Yang, F. S. Gu, An improved cyclic modulation spectral analysis based on the CWT and its application on broken rotor bar fault diagnosis for induction motors, Appl. Sci. 2019, 9, 3902DOI
5 
P. Konar, M. Saha, J. Sil, April 2013, Fault diagnosis of induction motor using CWT and rough-set theory, 2013 IEEE Symp. on Computational Intelligence in Control and Automation (CICA), pp. 17-23DOI
6 
Nor, Ahmad Kamal Bin Mohd, Srinivasa Rao Pedapait, Masdi Muhammad, Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review, arXiv preprint arXiv:2107.03869 (2021)Google Search
7 
Md Junayed Hasan, Muhammad Sohaib, Jong-Myon Kim, An Explainable AI-Based Fault Diagnosis Model for Bearings, Sensors 21.12 (2021): 4070DOI
8 
Zhou Weidong, Li Yingyuan, 2001, EEG real-time feedback based on STFT and coherence analysis, In 2001 Con- ference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, pp. 1869-1871. IEEEDOI
9 
N. G. Kingsbury, J. F. A. Magarey., 1997, Wavelet transforms in image processing, in Proc. 1st European Conf. Signal Anal. Prediction, Prague, June 24-27:, pp. 23-24DOI
10 
Pratyay Konar, Jaya Sil, Paramita Chattopadhyay, 2015, Knowledge extraction using data mining for multi-class fault diagnosis of induction motor, Neurocomputing 166, pp. 14-25DOI
11 
Siyu Shao, 2019, DCNN-based multi-signal induction motor fault diagnosis, IEEE Transactions on Instrumenta- tion and Measurement 69.6: 2658-2669DOI
12 
M. Frigo, S. G. Johnson, 1998, FFTW: An adaptive software architecture for the FFT, in Proc. IEEE Int. Conf. Acou- stics, Speech, and Signal Processing, Vol. 3, pp. 1381-1384DOI
13 
Oct. 2021, Time-Frequency Gallery, Mathworks, last modifiedGoogle Search
14 
D. M. Hawkins, 2004, The Problem of Overfitting. J. Chem. Inf. Comput. Sci. 44, J. Chem. Inf. Comput. Sci. 44, pp. 1-12DOI
15 
David Gunning, 2017, Explainable artificial intelligence, (xai). Defense Advanced Research Projects Agency (DARPA), nd WebDOI
16 
J. H. Han, S. U. Park, 2021, A Study on the Effectiveness of Current Data in Motor Mechanical Fault Diagnosis Using XAI, ICEMS 2021 conference paper, in pressDOI
17 
N. Mehaland, R. Dahiya, 2007, Motor current signature analysis and its applications in induction motor fault diagnosis, Int. J. Syst. Appl., Eng. Develop., Vol. 2, No. 1, pp. 29-35Google Search
18 
N. Feki, G. Clerc, P. Velex, Feb 2013, Gear and motor fault modeling and detection based on motor current analysis, Elect. Power Syst. Res., Vol. 95, pp. 28-37DOI
19 
J. H. Han, D. J. Choi, S. U. Park, S. K. Hong, Oct 2019, A Study on Motor Poor Maintenance Detection Based on DT-CNN, ICCAS 2019 Conference paper, pp. 1234-1237DOI
20 
J. H. Han, D. J. Choi, S. U. Park, S. K. Hong, 2020, Hyper-parameter Optimization Using a Genetic Algorithm Consi- dering Verification Time in a Convolutional Neural Net- work, Journal of Electrical Engineering & Technology, Vol. vol 15, pp. 721-726DOI