The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives
Abstract
Intelligent fault diagnosis has been a promising way for condition-based maintenance. However, the small sample problem has limited the application of intelligent fault diagnosis into real industrial manufacturing. Recently, the generative adversarial network (GAN) is considered as a promising way to solve the problem of small sample. For this purpose, this paper reviews the related research results on small-sample-focused fault diagnosis methods using the GAN. First, a systematic description of the GAN, and its variants, including structure-focused and loss-focused improvements, are introduced in the paper. Second, the paper reviews the related GAN-based intelligent fault diagnosis methods and classifies these studies into three main categories, deep generative adversarial networks for data augmentation, adversarial training for transfer learning, and other application scenarios (including GAN for anomaly detection and semi-supervised adversarial learning). Finally, the paper discusses several limitations of existing studies and points out future perspectives of GAN-based applications.
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Cites
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Proceedings of the 25th international conference on Machine learning
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“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
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Energy-based Generative Adversarial Network
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CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., & Pinheiro, P. R. (2020) · IEEE Access · doi:10.1109/access.2020.2994762 · Cited by 759
A comprehensive review on convolutional neural network in machine fault diagnosis
Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.088 · Cited by 575
Cited by
Shao, H., Li, W., Cai, B., Wan, J., Xiao, Y., & Yan, S. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3232766 · Cited by 284
Zheng, S., Pan, K., Liu, J., & Chen, Y. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110382 · Cited by 127
Metadata
- Authors
- Pan, T., Chen, J., Zhang, T., Liu, S., He, S., & Lv, H.
- Venue
- ISA Transactions
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 128
- Issue
- Pt B
- Pages
- 1-10
- ISSN
- 0019-0578 (ISSN-L)
- OpenAlex
- W4200533191
- Cited by
- 283
- References
- 64
- FWCI
- 6.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pan, T., Chen, J., Zhang, T., Liu, S., He, S., & Lv, H. (2022). Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives. ISA Transactions. https://doi.org/10.1016/j.isatra.2021.11.040
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