The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation
Abstract
End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited samples is challenging in industrial practice. The existing limited samples methods usually focus on the data distribution or learning strategy with particularity. Generative adversarial network (GAN) provides a data generation solution with portability in fault diagnosis with limited samples. However, GAN has problems with gradient vanishing, weak extraction of global features, and redundant training. This article proposes a dual-threshold attention-guided GAN (DTAGAN) to generate high-quality infrared thermal (IRT) images to assist fault diagnosis. First, Wasserstein distance and gradient penalty are combined to design loss function to avoid gradient vanishing. Second, attention-guided GAN is constructed to extract global thermal-correlation features of IRT images. Finally, dual-threshold training mechanism is developed to improve the generation quality and training efficiency. The comparative experiments show that DTAGAN is superior to comparison methods in fault diagnosis of rotor-bearing system under speed fluctuation and limited samples.
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Cites
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477
Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018) · arXiv · doi:10.48550/arxiv.1805.08318 · Cited by 3,353
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
Self-citationLi, W., Zhong, X., Shao, H., Cai, B., & Yang, X. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101552 · Cited by 340
Self-citationA novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
Shao, H., Lin, J., Zhang, L., Galar, D., & Kumar, U. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.03.008 · Cited by 320
Self-citationConvolutional neural network based bearing fault diagnosis of rotating machine using thermal images
Choudhary, A., Mian, T., & Fatima, S. (2021) · Measurement · doi:10.1016/j.measurement.2021.109196 · Cited by 292
Cao, H., Shao, H., Zhong, X., Deng, Q., Yang, X., & Xuan, J. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.11.016 · Cited by 290
Self-citationPan, T., Chen, J., Zhang, T., Liu, S., He, S., & Lv, H. (2022) · ISA Transactions · doi:10.1016/j.isatra.2021.11.040 · Cited by 283
Yang, B., Lei, Y., Jia, F., Li, N., & Du, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2953010 · Cited by 282
Cited by
Zhao, K., Hu, J., Shao, H., & Hu, J. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109246 · Cited by 149
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Metadata
- Authors
- Shao, H., Li, W., Cai, B., Wan, J., Xiao, Y., & Yan, S.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2023-09
- Volume
- 19
- Issue
- 9
- Pages
- 9933-9942
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4319992826
- Cited by
- 284
- References
- 29
- FWCI
- 30.88
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shao, H., Li, W., Cai, B., Wan, J., Xiao, Y., & Yan, S. (2023). Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2022.3232766
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