Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
Source abstract
Abstract
Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Decision-Making Techniques · Fault Detection and Control Systems · Technology and Security Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200090, China
Funding
Shanghai Sailing Program · Shanghai Rising-Star Program
References (32)
Show source-provided references
1. Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
2. A Fault Diagnosis Method for Rotating Machinery Based on CNN with Mixed Information10.1109/TII.2022.3224979 ↗
3. Zhang, W., Peng, G., Li, C., Chen, Y., and Zhang, Z. (2017). A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors, 17.10.20944/preprints201701.0132.v1 ↗
4. Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis with Limited Data10.1109/TII.2021.3112504 ↗
5. Hybrid Computing Techniques for Fault Detection and Isolation, a Review10.1016/j.compeleceng.2014.12.015 ↗
6. Intelligent Fault Diagnosis of Rolling Bearings under Varying Operating Conditions Based on Domain-Adversarial Neural Network and Attention Mechanism10.1016/j.isatra.2022.04.026 ↗
7. Inayat, U., Zia, M.F., Mahmood, S., Khalid, H.M., and Benbouzid, M. (2022). Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects. Electronics, 11.10.3390/electronics11091502 ↗
8. Siddique, M.F., Ahmad, Z., Ullah, N., and Kim, J. (2023). A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection. Sensors, 23.10.3390/s23198079 ↗
9. Hierarchical Adaptive Deep Convolution Neural Network and Its Application to Bearing Fault Diagnosis10.1016/j.measurement.2016.07.054 ↗
10. Ullah, N., Ahmad, Z., Siddique, M.F., Im, K., Shon, D.K., Yoon, T.H., Yoo, D.S., and Kim, J.M. (2023). An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning. Sensors, 23.10.3390/s23218850 ↗
11. Task Failure Prediction in Cloud Data Centers Using Deep Learning10.1109/TSC.2020.2993728 ↗
12. Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
13. Huo, C., Jiang, Q., Shen, Y., Zhu, Q., and Zhang, Q. (2023). Enhanced Transfer Learning Method for Rolling Bearing Fault Diagnosis Based on Linear Superposition Network. Eng. Appl. Artif. Intell., 121.10.1016/j.engappai.2023.105970 ↗
14. Partial Transfer Learning in Machinery Cross-Domain Fault Diagnostics Using Class-Weighted Adversarial Networks10.1016/j.neunet.2020.06.014 ↗
15. A Fault Diagnosis Method of Bearings Based on Deep Transfer Learning10.1016/j.simpat.2022.102659 ↗
16. Unsupervised Adversarial Adaptation Network for Intelligent Fault Diagnosis10.1109/TIE.2019.2956366 ↗
17. Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis with Imbalanced Data
18. A Novel Deep Convolution Multi-Adversarial Domain Adaptation Model for Rolling Bearing Fault Diagnosis10.1016/j.measurement.2022.110752 ↗
19. Chen, Z., Xu, J., Alippi, C., Ding, S.X., Shardt, Y., Peng, T., and Yang, C. (2021). Graph Neural Network-Based Fault Diagnosis: A Review. arXiv.
20. Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5–10). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain.
21. Tang, H., Chen, K., and Jia, K. (2020, January 13–19). Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00875 ↗
22. A Survey on Adversarial Domain Adaptation10.1007/s11063-022-10977-5 ↗
23. Adversarial Domain Adaptation Convolutional Neural Network for Intelligent Recognition of Bearing Faults10.1016/j.measurement.2022.111150 ↗
24. Fault Diagnosis of Rotating Machinery Based on the Improved Multidimensional Normalization ResNet10.1109/TIM.2023.3293554 ↗
25. 1D Convolutional Neural Networks and Applications: A Survey10.1016/j.ymssp.2020.107398 ↗
26. Multi-Source Unsupervised Domain Adaptation for Object Detection10.1016/j.inffus.2021.09.011 ↗
27. MMFSL: A Novel Multimodal Few-Shot Learning Framework for Fault Diagnosis of Industrial Bearings10.1109/TIM.2023.3289549 ↗
28. Deep Prototypical Networks Based Domain Adaptation for Fault Diagnosis10.1007/s10845-020-01709-4 ↗
29. Csurka, G. (2017). Domain-Adversarial Training of Neural Networks. Domain Adaptation in Computer Vision Applications, Springer International Publishing.10.1007/978-3-319-58347-1 ↗
30. Tang, H., and Jia, K. (February, January 27). Discriminative Adversarial Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
31. Kurmi, V.K., and Namboodiri, V.P. (2019, January 14–19). Looking Back at Labels: A Class Based Domain Adaptation Technique. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.10.1109/IJCNN.2019.8852199 ↗
32. Visualizing Data Using T-SNE