The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
Abstract
Bearing fault diagnosis is of significance to enhance the reliability and security of electric locomotive. In this paper, a novel convolutional deep belief network (CDBN) is proposed for bearing fault diagnosis. First, an auto-encoder is used to compress data and reduce the dimension. Second, a novel CDBN is constructed with Gaussian visible units to learn the representative features. Third, exponential moving average is employed to improve the performance of the constructed deep model. The proposed method is applied to analyze experimental signals collected from electric locomotive bearings. The results show that the proposed method is more effective than the traditional methods and standard deep learning methods.
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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
Lei, 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
Cited by
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
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
Deep Residual Shrinkage Networks for Fault Diagnosis
Zhao, M., Zhong, S., Fu, X., Tang, B., & Pecht, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2943898 · Cited by 1,447
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-citationHan, Y., Tang, B., & Deng, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.01.012 · Cited by 111
Metadata
- Authors
- Shao, H., Jiang, H., Zhang, H., & Liang, T.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2018-03
- Volume
- 65
- Issue
- 3
- Pages
- 2727-2736
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2746111230
- Cited by
- 488
- References
- 2
- FWCI
- 30.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shao, H., Jiang, H., Zhang, H., & Liang, T. (2018). Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2017.2745473
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