The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network
Open accessAbstract
Deep neural networks present very competitive results in mechanical fault diagnosis. However, training deep models require high computing power while the performance of deep architectures in extracting discriminative features for decision making often suffers from the lack of sufficient training data. In this paper, a transferable convolutional neural network (CNN) is proposed to improve the learning of target tasks. First, a one-dimensional CNN is constructed and pretrained based on large source task datasets. Then a transfer learning strategy is adopted to train a deep model on target tasks by reusing the pretrained network. Thus, the proposed method not only utilizes the learning power of deep network but also leverages the prior knowledge from the source task. Four case studies are considered and the effects of transfer layers and training sample size on classification effectiveness are investigated. Results show that the proposed method exhibits better performance compared with other algorithms.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
Self-citationA new convolutional neural network-based data-driven fault diagnosis method
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774777 · Cited by 2,222 · cited 2×
“In order to examine the superiority of the modified TCNN, a comparison of TCNN with CNN-Wen [12], 2-”
BackgroundMethodZhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017) · Sensors · doi:10.3390/s17020425 · Cited by 1,925
Transfer learning with neural networks for bearing fault diagnosis in changing working conditions
Zhang, R., Tao, H., Wu, L., & Guan, Y. (2017) · IEEE Access · doi:10.1109/access.2017.2720965 · Cited by 378
Liu, R., Meng, G., Yang, B., Sun, C., & Chen, X. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2645238 · Cited by 345
Cited by
Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3005965 · Cited by 452
Adversarial Multiple-Target Domain Adaptation for Fault Classification
Ragab, M., Chen, Z., Wu, M., Li, H., Kwoh, C.-K., Yan, R., & Li, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3009341 · Cited by 90
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Metadata
- Authors
- Chen, Z., Gryllias, K., & Li, W.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-01
- Volume
- 16
- Issue
- 1
- Pages
- 339-349
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2946048316
- Cited by
- 342
- References
- 9
- FWCI
- 21.51
- Type
- journal article
- Language
- en
- Open access
- green
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