The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks
Abstract
Abstract In recent years, the technique of machine learning or deep learning has been employed in intelligent fault diagnosis methods to achieve much success using massive labeled data. However, it is generally difficult or expensive to label the monitoring data in practical engineering due to its complex working conditions. Therefore, an unsupervised fault diagnosis method is proposed in this paper for rolling bearings, which incorporates short-time Fourier transform (STFT) as well as categorical generative adversarial networks (CatGAN). The proposed method first adopts STFT to transform raw 1-D vibration signals into 2-D time-frequency maps to serve as the input of CatGAN. Then, it obtains a CatGAN model via an adversarial training process to generate fake samples with a similar distribution to the maps extracted by STFT and cluster the input samples into certain categories. Furthermore, the performance of the proposed ST-CatGAN method is verified using a classic rotating machinery dataset, and the experimental results demonstrate its high diagnosis accuracy and strong robustness against the motor load changes.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
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
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
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
Zhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017) · Sensors · doi:10.3390/s17020425 · Cited by 1,925
Self-citationLei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191
Feng, Z., Liang, M., & Chu, F. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.01.017 · Cited by 998
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Cited by
CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis
Ruan, D., Wang, J., Yan, J., & Gühmann, C. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101877 · Cited by 435
Chen, J., Huang, R., Chen, Z., Mao, W., & Li, W. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110239 · Cited by 267
Metadata
- Authors
- Tao, H., Wang, P., Chen, Y., Stojanovic, V., & Yang, H.
- Venue
- Journal of the Franklin Institute
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 357
- Issue
- 11
- Pages
- 7286-7307
- ISSN
- 0016-0032 (ISSN-L)
- OpenAlex
- W3026117520
- Cited by
- 315
- References
- 8
- FWCI
- 23.22
- Type
- journal article
- Language
- en
- Open access
- closed
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