A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | X. Chen, X. Li, S. Yu, Y. Lei, N. Li, and B. Yang, “Dynamic vision enabled contactless cross-domain machine fault diagnosis with neuromorphic computing,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 788–790, Mar. 2024. doi: 10.1109/JAS.2023.124107
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| [1] |
B. A. Tama, M. Vania, S. Lee, and S. Lim, “Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals,” J. Dynamics,Monitoring and Diagnostics, vol. 1, no. 2, pp. 57–65, Oct. 2022.
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| [2] |
D. Peng, H. Wang, W. Desmet, and K. Gryllias, “RMA-CNN: A residual mixed-domain attention CNN for bearings fault diagnosis and its time-frequency domain interpretability,” J. Dynamics,Monitoring and Diagnostics, vol. 2, no. 2, pp. 115–132, Apr. 2023.
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| [3] |
S. Tsuruta, K. Wakimoto, T. Nakamura, et al., “Advancing predictive maintenance: A study of domain adaptation for fault identification in gearbox components, ” in Proc. PHM Society Asia-Pacific Confer., Sep. 2023, vol. 4, p. 1.
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| [4] |
D. Helm and M. Timusk, “Wavelet denoising applied to hardware redundant systems for rolling element bearing fault detection,” J. Dynamics,Monitoring and Diagnostics, vol. 2, no. 2, pp. 102–114, Jun. 2023.
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| [5] |
R. A. Kanai, R. G. Desavale, and S. P. Chavan, “Experimental-based fault diagnosis of rolling bearings using artificial neural network,” ASME. J. Tribol, vol. 138, no. 3, p. 031103, Apr. 2016. doi: 10.1115/1.4032525
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| [6] |
K. Zhong, M. Han, and B. Han, “Data-driven based fault prognosis for industrial systems: A concise overview,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 330–345, Mar. 2020. doi: 10.1109/JAS.2019.1911804
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| [7] |
Y. Lei, N. Li, and X. Li, Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems, Singapore: Springer, 2023.
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| [8] |
J. Lee, M. Kim, J. U. Ko, J. H. Jung, and K. H. Sun, “Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery,” Reliability Engineering &System Safety, vol. 218, part. B, p. 108186, Feb. 2022.
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| [9] |
W. Mao, G. Wang, L. Kou, and X. Liang, “Deep domain-adversarial anomaly detection with one-class transfer learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 524–546, Feb. 2023. doi: 10.1109/JAS.2023.123228
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| [10] |
S. Li, C. Zang, L. Du, et al., “Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering,” Green Energy and Intelligent Transportation, vol. 1, no. 1, p. 100009, Jun. 2022. doi: 10.1016/j.geits.2022.100009
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| [11] |
B. Liu, C. Peng, and M. Zhu, “A vision-based data-driven method of fault diagnosis for rolling bearings in rotating machinery,” Advances in Guidance,Navigation and Control, vol. 644, pp. 2345–2355, Oct. 2021.
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| [12] |
X. Li, S. Yu, Y. Lei, et al., “Intelligent machinery fault diagnosis with event-based camera,” IEEE Trans. Industrial Informatics, vol. 20, no. 1, pp. 380–389, 2024.
|
| [13] |
J. Wang, T. Li, C. Sun, et al., “Improved spiking neural network for intershaft bearing fault diagnosis,” J. Manufacturing Systems, vol. 65, pp. 208–219, Oct. 2022. doi: 10.1016/j.jmsy.2022.09.003
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| [14] |
V. Kholkin, O. Druzhina, V. Vatnik, et al., “Comparing reservoir artificial and spiking neural networks in machine fault detection tasks,” Big Data and Cognitive Computing, vol. 7, no. 2, p. 110, Jun. 2023. doi: 10.3390/bdcc7020110
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| [15] |
Z. Xu, Y. Ma, Z. Pan, and X. Zheng, “Deep spiking residual shrinkage network for bearing fault diagnosis,” IEEE Trans. Cyber., 2022.
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| [16] |
C. Zhang, Z. Xiao, and Z. Sheng, “A bearing fault diagnosis method based on a convolutional spiking neural network with spatial-temporal feature-extraction capability,” Transportation Safety and Environment, vol. 5, no. 2, p. tdac050, Apr. 2023. doi: 10.1093/tse/tdac050
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| [17] |
W. Zhang and X. Li, “Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions,” Structural Health Monitoring, vol. 21, no. 4, pp. 1329–1344, 2022. doi: 10.1177/14759217211029201
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| [18] |
X. Li, W. Zhang, and Q. Ding, “A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning,” Neurocomputing, vol. 310, pp. 77–95, Oct. 2018. doi: 10.1016/j.neucom.2018.05.021
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