华中科技大学主页平台管理系统 KAIBO ZHOU--英文主页-- K. Zhou, C. Yang, J. Liu and Q. Xu, "Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis," in IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10595-10604, Oct. 2022, doi: 10.1109/TIE.2021.3121748.

KAIBO ZHOU

Recommended Ph.D.Supervisor Recommended MA Supervisor

Paper Publications

K. Zhou, C. Yang, J. Liu and Q. Xu, "Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis," in IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10595-10604, Oct. 2022, doi: 10.1109/TIE.2021.3121748.

Release time:2022-06-21 Hits:

Indexed by:Journal paper DOI number:10.1109/TIE.2021.3121748.

Pre One:C. Yang, J. Liu, K. Zhou and X. Jiang, "Semi-supervised machine fault diagnosis fusing unsupervised graph contrastive learning," in IEEE Transactions on Industrial Informatics, 2022, doi: 10.1109/TII.2022.3220847.

Next One:Shangyuan Li, Kaibo Zhou, Luanxiao Zhao, Qi Xu, Jie Liu*. An improved lithology identification approach based on representation enhancement by logging feature decomposition, selection and transformation. Journal of Petroleum Science and Engineering, 2022, 209:109842