The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images
Open accessAbstract
The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.
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Citations
Cites
Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2864759 · Cited by 1,635
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
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.12.051 · Cited by 879
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Zeiler, M. D., & Fergus, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3557 · Cited by 755
Sun, C., Ma, M., Zhao, Z., Tian, S., Yan, R., & Chen, X. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2881543 · Cited by 519
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
Shao, H., Jiang, H., Zhang, H., & Liang, T. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2745473 · Cited by 488
Self-citationIntelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network
Chen, Z., Gryllias, K., & Li, W. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2917233 · Cited by 342
Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data
Ma, M., Sun, C., & Chen, X. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2793246 · Cited by 304
Pan, J., Zi, Y., Chen, J., Zhou, Z., & Wang, B. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2767540 · Cited by 294
A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks
Xia, M., Li, T., Shu, T., Wan, J., de Silva, C. W., & Wang, Z. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2868687 · Cited by 291
Self-citation
Cited by
Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions
Li, T., Zhao, Z., Sun, C., Yan, R., & Chen, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3075016 · Cited by 280
Lakhan, A., Mohammed, M. A., Nedoma, J., Martinek, R., Tiwari, P., Vidyarthi, A., Alkhayyat, A., & Wang, W. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3165945 · Cited by 224
Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection
Maliuk, A. S., Prosvirin, A. E., Ahmad, Z., Kim, C. H., & Kim, J.-M. (2021) · Sensors · doi:10.3390/s21196579 · Cited by 44
Metadata
- Authors
- Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-05
- Volume
- 17
- Issue
- 5
- Pages
- 3488-3496
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3039216919
- Cited by
- 452
- References
- 26
- FWCI
- 32.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J. (2021). Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2020.3005965
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