A Multi-source Sensors Framework for Mechanical Fault Diagnosis Under Strong Noise
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67295-8_25
Reference8 articles.
1. Tian, J., Han, D., Karimi, H.R., Zhang, Y., Shi, P.: A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data. Neural Netw. 173, 106167 (2024)
2. Yu, Y., Karimi, H.R., Shi, P., Peng, R., Zhao, S.: A new multi-source information domain adaption network based on domain attributes and features transfer for cross-domain fault diagnosis. Mech. Syst. Signal Process. 211, 111194 (2024)
3. Yu, Y., Guo, L., Gao, H., He, Y., You, Z., Duan, A.: FedCAE: a new federated learning framework for edge-cloud collaboration based machine fault diagnosis. IEEE Trans. Industr. Electron. 71, 4108–4119 (2023)
4. Yu, Y., He, Y., Karimi, H.R., Gelman, L., Cetin, A.E.: A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis. Neural Netw. 106518 (2024)
5. Tian, J., Han, D., Li, M., Shi, P.: A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis. Knowl.-Based Syst. 243, 108466 (2022)
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