w   机电设备智能运维相关论文(SCI论文9篇，EI论文2篇)

[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. 

[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.

[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.

[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.

[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. 

[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.

[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)

[8]  基于双级对齐部分迁移网络的旋转设备故障诊断，电子学报

[9]  Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning, Expert Systems with Applications 通讯

[10] Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features, Structural Health Monitoring 通讯

[11] Dynamics Simulation-driven Fault Diagnosis of Rolling Bearings Using Security Transfer Support Matrix Machine, Reliability Engineering & System Safety 共同通讯