과제정보
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2023S1A5A8080527)
참고문헌
- D. H. Pandya, S. H. Upadhyay and S. P. Harsha, "Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN," Expert Syst. Appl, vol. 40, no. 11, pp. 4137-4145, Aug 2013. DOI:https://doi.org/10.1016/j.eswa.2013.01.033
- S. W. Choi, C. Lee, J.-M. Lee, J. H. Park and I.-B. Lee, "Fault detection and identification of nonlinear processes based on kernel PCA," Chemometr. Intell. Lab, vol. 75, no. 1, pp. 55-67, Jan 2005. DOI: https://doi.org/10.1016/j.chemolab.2004.05.001
- H. Hu, B. Tang, X. Gong, W. Wei and H. Wang, "Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks," IEEE Trans. Ind. Informa., vol. 13, no. 4, pp. 2106-2116, Aug 2017. DOI: https://doi.org/10.1109/TII.2017.2683528
- X. Liu, L. Ma and J. Mathew, "Machinery fault diagnosis based on feature level fuzzy integral data fusion techniques," presented at the IEEE Int. Conf. Ind. Informat, Singapore, pp. 857-862, 2006, DOI: https://doi.org/10.1109/INDIN.2006.275689
- L. Ren, W. Lv, S. Jiang and Y. Xiao, "Fault diagnosis using a joint model based on sparse representation and SVM," IEEE Trans. Instrum. Meas, vol. 65, no. 10, pp. 2313-2320, Oct 2016. DOI: https://doi.org/10.1109/TIM.2016.2575318
- M. Heydarzadeh, S. H. Kia, M. Nourani, H. Henao and G. Capolino, "Gear fault diagnosis using discrete wavelet transform and deep neural networks," presented at the 42nd Annu. Conf. IEEE Ind. Electron. Soc, Florence, Italy, pp. 1494-1500, 2016. DOI: https://doi.org/10.1109/IECON.2016.7793549
- C. Sun, M. Ma, Z. Zhao and X. Chen, "Sparse deep stacking network for fault diagnosis of motor," IEEE Trans. Ind. Informa., vol. 14, no. 7, pp. 3261-3270, Jul 2018. DOI: https://doi.org/10.1109/TII.2018.2819674
- X. Yuan, J. Zhou, B. Huang, Y. Wang, C. Yang and W. Gui, "Hierarchical quality-relevant feature representation for soft sensor modeling: A novel deep learning strategy," IEEE Trans. Ind. Informat, vol. 16, no. 6, pp. 3721-3730, Jun 2020. DOI: https://doi.org/10.1109/TII.2019.2938890
- Y. Wang, Z. Pan, X. Yuan, C. Yang and W. Gui, "A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network," ISA Trans, vol. 96, pp. 457-467, Jan 2020. https://doi.org/10.1016/j.isatra.2019.07.001
- X. Yuan, L. Li and Y. Wang, "Nonlinear dynamic soft sensor modeling with supervised long short-term memory network," IEEE Trans. Ind. Informat, vol. 16, no. 5, pp. 3168-3176, May 2020. DOI: https://doi.org/10.1109/TII.2019.2902129
- S. Shao, S. McAleer, R. Yan,and P. Baldi, "Highly accurate machine fault diagnosis using deep transfer learning," IEEE Trans. Ind. Informat, vol. 15, no. 4, pp. 2446-2455, Apr 2019. DOI: https://doi.org/10.1109/TII.2018.2864759
- W. Sun, R. Zhao, R. Yan, S. Shao and X. Chen, "Convolutional discriminative feature learning for induction motor fault diagnosis," IEEE Trans. Ind. Informat, vol. 13, no. 3, pp. 1350-1359, Jun 2017. DOI: https://doi.org/10.1109/TII.2017.2672988
- R. Liu, G. Meng, B. Yang, C. Sun and X. Chen, "Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine," IEEE Trans. Ind. Informat, vol. 13, no. 3, pp. 1310-1320, Jun 2017. DOI: https://doi.org/10.1109/TII.2016.2645238
- R. Liu, F. Wang, B. Yang and S. J. Qin, "Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions," IEEE Trans. Ind. Informat, vol. 16, no. 6, pp. 3797-3806, Jun 2020. DOI: https://doi.org/10.1109/TII.2019.2941868
- W. Samek, T. Wiegand and K.-R. Muller, "Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models," arXiv:1708.08296, 2017. DOI: https://doi.org/10.48550/arXiv.1708.08296
- D. Gunning, "Explainable artificial intelligence (XAI)," Defense Advanced Research Projects Agency, DARPA/I20, 2017.
- M. S. Kim, J. P. Yun and P. G. Park, "An Explainable Convolutional Neural Network for Fault Diagnosis in Linear Motion Guide," IEEE Trans. Ind. Informat, vol. 17, no. 6, pp. 4036-4045, 2021. DOI: https://doi.org/10.1109/TII.2020.3012989
- J. Ahn, "An Explainable 1D-CNN Deep Learning Method for Fault Diagnosis of Rotating Machinery Using FFT-CAM," Master's Thesis, Graduate School of Industrial Engineering, Kyung Hee University, 2021.
- G. Manhertz and A. Bereczky, "STFT spectrogram based hybrid evaluation method for rotating machine transient vibration analysis," Mech. Syst. Signal Process, vol. 154, 107583, 2021. DOI: (CrossRef Link)
- J. Han, S. Park and S. Hong, "Performance Evaluation of the Continuous Wavelet Transformation Data in Motor Fault Diagnosis through XAI Algorithm," Trans. Korean Inst. Electr. Eng, vol. 71, no. 1, pp. 225-232, 2022.
- K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2015. DOI: https://doi.org/10.1016/j.ymssp.2020.107583
- Y. Kim, H. Jeon and Y. K. Kim, "A Comparison Study of Ball Bearing Fault Diagnosis and Classification Analysis Using XAI Grad-CAM," Trans. Korean Inst. Electr. Eng, vol. 71, no. 9, pp. 1315-1325, 2022. ISSN 1975-8359 [Print] / ISSN 2287-4364 [Online].
- B. Zhou, A. Khosla, A. Lapedriza, A. Oliva and A. Torralba, "Learning deep features for discriminative localization," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pp. 2921-2929, 2016.
- R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh and D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," in Proc. IEEE Int. Conf. Comput. Vis, pp. 618-626, 2017.
- Truman, "What Is a Confusion Matrix?" Truman's Blog. [Online:https://truman.tistory.com/179]