Open Access
Issue |
Int. J. Metrol. Qual. Eng.
Volume 15, 2024
|
|
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Article Number | 15 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/ijmqe/2024004 | |
Published online | 14 August 2024 |
- J. Zhuang, J. Yan, C. Huang, M. Jia, Residual attention temporal recurrent network for fault diagnosis of gearboxes under limited labeled data, Eng. Appl. Artif. Intel. 129, 107539 (2024) [CrossRef] [Google Scholar]
- D. Huo, Y. Kang, B. Wang, G. Feng, J. Zhang, H. Zhang, Gear fault diagnosis method based on multi-sensor information fusion and VGG, Entropy 24, 1618 (2022) [CrossRef] [PubMed] [Google Scholar]
- C. Yang, B. Cai, Q. Wu, C. Wang, W. Ge, Z. Hu, L. Wang, Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data, J. Ind. Inf. Integr. 33, 100469 (2023) [Google Scholar]
- C. Yang, B. Cai, R. Zhang, Z. Zou, X. Kong, X. Shao, Y. Liu, H. Shao, K. J. Akbar, Cross-validation enhanced digital twin driven fault diagnosis methodology for minor faults of subsea production control system, Mech. Syst. Signal Pr. 204, 110813 (2023) [CrossRef] [Google Scholar]
- L. Zou, K.J. Zhuang, A. Zhou, J. Hu, Bayesian optimization and channel-fusion-based convolutional autoencoder network for fault diagnosis of rotating machinery, Eng. Struct. 280, 115708 (2023) [CrossRef] [Google Scholar]
- D. Zou, Z. Li, H. Quan, Q. Peng, S. Wang, Z. Hong, J. Yin, Transformer fault classification for diagnosis based on DGA and deep belief network, Energy Rep. 12, 250–256 (2023) [CrossRef] [Google Scholar]
- Y. Dong, C. Wen, Z. Wang, A motor bearing fault diagnosis method based on multi-source data and one-dimensional lightweight convolution neural network, Proc. Inst. Mech. Eng. 237, 272–283 (2023) [Google Scholar]
- Z. Wang, C. Wen, Y. Dong, A method for rolling bearing fault diagnosis based on GSC-MDRNN with multi-dimensional input, Meas. Sci. Technol. 34, 055901 (2023) [CrossRef] [Google Scholar]
- H. Chen, W. Meng, Y. Li, Q. Xiong, An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model, Meas. Sci. Technol. 34, 045013 (2023) [CrossRef] [Google Scholar]
- C. Wu, P. Jiang, C. Ding, F. Feng, Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network, Comput. Ind. 108, 53–61 (2019) [CrossRef] [Google Scholar]
- J. Jiao, M. Zhao, J. Lin, C. Ding, Deep coupled dense convolutional network with complementary data for intelligent fault diagnosis, IEEE Trans. Ind. Electr. 66, 9858–9867 (2019) [CrossRef] [Google Scholar]
- J. Zhang, Q. Zhang, X. Qin, Y. Sun, Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN, Struct. Health Monit. 23, 324–342 (2024) [CrossRef] [Google Scholar]
- X. Zhang, H. Li, W. Meng, Y. Liu, P. Zhou, C. He, Q. Zhao, Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network, J. Braz. Soc. Mech. Sci. 44, 462 (2022) [CrossRef] [Google Scholar]
- Y. Dong, C. Wen, Z. Wang, A motor bearing fault diagnosis method based on multi-source data and one-dimensional lightweight convolution neural network, Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 273, 272–283 (2023) [Google Scholar]
- G. Wang, T. Zhang, Z. Hu, M. Zhang, A novel lightweight unsupervised multi-branch domain adaptation network for bearing fault diagnosis under cross-domain conditions, J. Fail. Anal. Prev. 23, 1645–1662 (2023) [CrossRef] [Google Scholar]
- K. You, G. Qiu, Y. Gu, An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults, Meas. Sci. Technol. 34, 094001 (2023) [CrossRef] [Google Scholar]
- J. Tong, C. Liu, J. Zheng, H. Pan, Multi-sensor information fusion and coordinate attention-based fault diagnosis method and its interpretability research, Eng. Appl. Artif. Intel. 124, 106614 (2023) [CrossRef] [Google Scholar]
- C. He, D. He, Z. Lao, Z. Wei, Z. Xiang, W. Xiang, A lightweight model for train bearing fault diagnosis based on multiscale attentional feature fusion, Meas. Sci. Technol. 34, 025113 (2023) [CrossRef] [Google Scholar]
- X. Li, P. Yuan, Wang, D. Li, Z. Xie, Kong, An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution, Meas. Sci. Technol. 34, 095401 (2023) [CrossRef] [Google Scholar]
- S. Djaballah, K. Meftah, K. Khelil, M. Sayadi, Deep transfer learning for bearing fault diagnosis using CWT time-frequency images and convolutional neural networks, J. Fail. Anal. Prev. 23, 1046–1058 (2023) [CrossRef] [Google Scholar]
- X. Li, T. Yu, D. Li, X. Wang, C. Shi, Z. Xie, X. Kong, A migration learning method based on adaptive batch normalization improved rotating machinery fault diagnosis, Sustainability 15, 8034 (2023) [CrossRef] [Google Scholar]
- Y. Zhou, X. Long, M. Sun, Z. Chen, Bearing fault diagnosis based on Gramian angular field and DenseNet, Math. Biosci. Eng. 19, 14086–14101 (2022) [CrossRef] [Google Scholar]
- A. Howard, M. Sandler, G. Chu, L.C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q.V. Le, H. Adam, Searching for mobilenetv3, in Proceedings of the IEEE/CVF International Conference on Computer Vision (2019), pp. 1314–1324 [Google Scholar]
- M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. C. Chen, Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation, in CoRR (2018) abs/1801.04381 [Google Scholar]
- Q. Hou, D. Zhou, J. Feng, Coordinate attention for efficient mobile network design, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021), pp. 13708–13717 [Google Scholar]
- S. Shao, S. McAleer, R. Yan, P. Baldi, Highly accurate machine fault diagnosis using deep transfer learning, IEEE Trans. Ind. Inform. 15, 2446–2455 (2019) [CrossRef] [Google Scholar]
- S. Woo, J. Park, J. Lee, I.S. Kweon, Cbam: Convolutional block attention module, in Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 3–19 [Google Scholar]
- K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in International Conference on Learning Representations (2014), pp. 1–14 [Google Scholar]
- K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [Google Scholar]
- Y. Huang, A. Liao, D. Hu, W. Shi, S. Zheng, Multi-scale convolutional network with channel attention mechanism for rolling bearing fault diagnosis, Measurement 203, 111935 (2022) [CrossRef] [Google Scholar]
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