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  • 俞昆 ( 讲师 )

    的个人主页 http://faculty.cumt.edu.cn/YK77/zh_CN/index.htm

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机电装备智能运维

w   机电装备智能运维相关论文(SCI论文6篇,EI论文1)

[1]      K. Yu, T. R. Lin, H. Ma, X. Li and X. Li, “A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning,” Mechanical Systems and Signal Processing, vol. 146, 107043, Jan. 2021. (ESI高被引论文,影响因子:6.823)

[2]      K. Yu, T.R. Lin and J. Tan, “A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster-Shafer theory,” Structural Health Monitoring, vol. 19, no. 1, pp. 240-261, Jan. 2020. (ESI高被引论文,影响因子:5.929)

[3]      K. Yu, Q. Fu, H. Ma, T. R. Lin, and X. Li, “Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis,” Structural Health Monitoring, vol. 20, no. 4, pp. 2182-2198, Jul. 2021. (影响因子:5.929)

[4]      K. Yu, H. Ma, T. R. Lin, and X. Li, “A consistency regularization based semi-supervised learning approach for intelligent fault diagnosis of rolling bearing,” Measurement, vol. 165, 107987, Dec. 2020. (影响因子:3.927)

[5]      K. Yu, T.R. Lin and J. Tan, “A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering,” Applied Acoustics, vol. 121, pp. 33-45, Jun. 2017. (影响因子:2.639)

[6]      K. Yu, H. Han, Q. Fu, H. Ma and J. Zeng, “Symmetric co-training based unsupervised domain adaptation approach for intelligent fault diagnosis of rolling bearing,” Measurement Science and Technology, vol. 31, no. 11, 115008 (15pp), Aug. 2020. (影响因子:2.046)

[7]      K. Yu, J. Tan and T.R. Lin, “Fault diagnosis of rolling element bearing using multi-scale Lempel-Ziv complexity and Mahalanobis distance criterion,” Journal of Shanghai Jiaotong University (Science), vol. 23, no. 5, pp. 696-701, Jun. 2018. (EI论文)


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