Otwarty dostęp

Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan


Zacytuj

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

A. G. Howard et al., “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint, 2017, doi: 10.48550/arXiv.1704.04861. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

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. Open DOISearch in Google Scholar

L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579-2605, 2008. Search in Google Scholar

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. Open DOISearch in Google Scholar

eISSN:
2083-7429
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Engineering, Introductions and Overviews, other, Geosciences, Atmospheric Science and Climatology, Life Sciences