References
- Y. H. Tan, J. D. Zhang, H. Tian, D. Y. Jiang, L. Guo, G. M. Wang, Y. J. Lin, “Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study,” Ocean Engineering, vol. 239, p. 109723, 2021, doi: 10.1016/j.oceaneng.2021.109723.
- G. H. Yan, Y. H. Hu, J. W. Jiang, “A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals,” Polish Maritime Research, vol. 29, no. 2, pp. 77-86, 2022, doi:10.2478/POMR-2022-0019.
- Y. Xie and T. Zhang, “Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition,” Shock and Vibration, vol. 2017, pp. 11-12, 2017, doi: 10.1155/2017/3084197.
- Z. Guan, Z. Liao, K. Li, and P. Chen, “A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network,” Sensors (Basel), vol. 19, no. 3, p. 591, 2019, doi: 10.3390/s19030591.
- M. Kuai, G. Cheng, Y. Pang, and Y. Li, “Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS,” Sensors (Basel), vol. 18, no. 3, p. 782, 2018, doi: 10.3390/s18030782.
- R. Nishat Toma, C.-H. Kim, and J.-M. Kim, “Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network,” Electronics, vol. 10, no. 11, p. 1248, 2021, doi: 10.3390/ELECTRONICS10111248.
- W. Jiang, Y. H. Xu, Z. Chen, N. Zhang, and J. Z. Zhou, “Fault diagnosis for rolling bearing using a hybrid hierarchical method based on scale-variable dispersion entropy and parametric t-SNE algorithm,” Measurement, vol. 191, p. 110843, 2022, doi: 10.1016/j.measurement.2022.110843.
- S. Zhou, M. H. Xiao, P. Bartos, M. Filip, and G. S. Geng, “Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network,” Shock and Vibration, vol. 2020, p. 8857307, 2020, doi: 10.1155/2020/8857307.
- X. C. Zhang, H. W. Li, W. Y. Meng, Y. F. Liu, P. Zhou, C. He, Q. B. Zhao, “Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 44, no. 10, p. 462, 2022, doi:10.1007/s40430-022-03759-6.
- A. G. Howard et al., “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint, 2017, doi: 10.48550/arXiv.1704.04861.
- M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510-4520, doi: 10.48550/arXiv.1801.04381.
- X. Y. Zhang, X. Y. Zhou, M. X. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6848-6856, doi: 10.48550/arXiv.1707.01083.
- N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “Shufflenet v2: Practical guidelines for efficient CNN architecture design,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 116-131, doi: 10.48550/arXiv.1807.11164.
- S. Z. Hou, W. Guo, Z. Q. Wang, and Y. T. Liu, “Deep-Learning-Based Fault Type Identification Using Modified CEEMDAN and Image Augmentation in Distribution Power Grid,” IEEE Sensors Journal, vol. 22, no. 2, pp. 1583-1596, 2022, doi: 10.1109/Jsen.2021.3133352.
- M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise,” in 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011, pp. 4144-4147, doi: 10.1109/ICASSP.2011.5947265.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017, doi: 10.1145/3065386.
- S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “CBAM: Convolutional block attention module,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3-19, doi: 10.48550/arXiv.1807.06521.
- J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141, doi: 10.48550/arXiv.1709.01507.
- L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579-2605, 2008.
- K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.