The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Fault diagnosis of rotating machinery based on recurrent neural networks
Abstract
Abstract Fault diagnosis of rotating machinery is essential for maintaining system performance and ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and achieved remarkable results in fault diagnosis. However, the temporal information from time-series signals is ignored by convolutional neural networks (CNNs) based methods. Besides, the robustness against the noise is essential to methods for fault diagnosis. Therefore, a novel method based on recurrent neural networks (RNNs) is proposed to identify fault types in rotating machinery in this paper. One-dimensional time-series vibration signals are first converted into two-dimensional images. Then, Gated Recurrent Unit (GRU) is introduced to exploit temporal information of time-series data and learn representative features from constructed images. A multilayer perceptron (MLP) is finally employed to implement fault recognition. Experimental results show that the proposed method achieves the best performance on two public datasets compared with existing work and exhibits the robustness against the noise.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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A 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
Self-citationJia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326
Self-citationMachine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., & Wang, J. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2733438 · Cited by 877
Neurocomputing: Foundations of Research
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Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders
Liu, H., Zhou, J., Zheng, Y., Jiang, W., & Zhang, Y. (2018) · ISA Transactions · doi:10.1016/j.isatra.2018.04.005 · Cited by 606
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Cited by
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhu, Z., Lei, Y., Qi, G., Chai, Y., Mazur, N., An, Y., & Huang, X. (2023) · Measurement · doi:10.1016/j.measurement.2022.112346 · Cited by 731
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Metadata
- Authors
- Zhang, Y., Zhou, T., Huang, X., Cao, L., & Zhou, Q.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 171
- Pages
- 108774
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W3109842401
- Cited by
- 339
- References
- 28
- FWCI
- 13.91
- Type
- journal article
- Language
- en
- Open access
- closed
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