代表性期刊论文 Selected Journal Publications

  1. Xiang Li, Yixiao Xu, Naipeng Li*, Bin Yang, Yaguo Lei, "Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks", IEEE/CAA Journal of Automatica Sinica, 2022.
  2. Xiang Li, Wei Zhang*, Hui Ma, Zhong Luo, Xu Li, “Degradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning”, IEEE Transactions on Neural Networks and Learning Systems, 2022. 
  3. Wei Zhang, Xiang Li*, “Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks with Data Privacy”, IEEE/ASME Transactions on Mechatronics, 2021. [ESI高被引论文 ESI highly cited paper]
  4. Wei Zhang, Xiang Li*, Hui Ma, Zhong Luo, Xu Li, “Universal Domain Adaptation in Fault Diagnostics with Hybrid Weighted Deep Adversarial Learning ”, IEEE Transactions on Industrial Informatics, 2021.
  5. Wei Zhang, Xiang Li*, Hui Ma, Zhong Luo, Xu Li, “Transfer Learning Using Deep Representation Regularization In Remaining Useful Life Prediction Across Operating Conditions”, Reliability Engineering & System Safety, 2021, 211, 107556.
  6. Wei Zhang, Xiang Li*, Hui Ma, Zhong Luo, Xu Li, “Open Set Domain Adaptation In Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning”, IEEE Transactions on Industrial Informatics, 2021, 17: 11, 7445-7455.
  7. 李杰, 李响, 许元铭, 杨绍杰, 孙可意. 工业人工智能及应用研究现状及展望. 自动化学报, 2020, 46(10): 2031−2044.
  8. Wei Zhang, Xiang Li*, Hui Ma, Zhong Luo, Xu Li, “Federated Learning for Machinery Fault Diagnosis with Dynamic Validation and Self-Supervision”, Knowledge-Based Systems, 2021, 213, 106679. [ESI高被引论文 ESI highly cited paper]
  9. Xiang Li, Wei Zhang, Qian Ding, and Xu Li*, “Diagnosing Rotating Machines with Weakly Supervised Data Using Deep Transfer Learning”, IEEE Transactions on Industrial Informatics, 2020, 16 (3), 1688-1697. [ESI高被引论文 ESI highly cited paper]
  10. Xiang Li*, Wei Zhang, Nan-Xi Xu, and Qian Ding, “Deep Learning-Based Machinery Fault Diagnostics with Domain Adaptation Across Sensors At Different Places”, IEEE Transactions on Industrial Electronics, 2020, 67 (8), 6785-6794. [ESI高被引论文 ESI highly cited paper]
  11. Xiang Li*, Wei Zhang, Qian Ding, and Jian-Qiao Sun, “Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation”, Journal of Intelligent Manufacturing, 2020, 31, 433-452. [ESI高被引论文 ESI highly cited paper]
  12. Wei Zhang, Xiang Li*, Xiao-Dong Jia, Hui Ma, Zhong Luo, and Xu Li, “Machinery fault diagnosis with imbalanced data using deep generative adversarial networks”, Measurement, 2020, 152, 107377. [ESI高被引论文 ESI highly cited paper]
  13. Xiang Li, Wei Zhang*, Hui Ma, Zhong Luo, Xu Li, “Partial Transfer Learning in Machinery Cross-Domain Fault Diagnostics Using Class-Weighted Adversarial Networks”, Neural Networks, 2020, 129, 313-322.
  14. Xiang Li*, Xiaodong Jia, Yinglu Wang, Shaojie Yang, Haodong Zhao, Jay Lee, “Industrial Remaining Useful Life Prediction by Partial Observation Using Deep Learning with Supervised Attention”, IEEE/ASME Transactions on Mechatronics, 2020, 25 (5), 2241-2251.
  15. Xiang Li, Wei Zhang, Hui Ma, Zhong Luo, Xu Li*, “Domain Generalization In Rotating Machinery Fault Diagnostics Using Deep Neural Networks”, Neurocomputing, 2020, 403, 409-420.
  16. Xiang Li, Wei Zhang*, “Deep Learning-Based Partial Domain Adaptation Method on Intelligent Machinery Fault Diagnostics”, IEEE Transactions on Industrial Electronics, 2020, 0, 0. [ESI高被引论文、热点论文 ESI highly cited and hot paper]
  17. Xiang Li, Xu Li, Hui Ma*, “Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery”, Mechanical Systems and Signal Processing, 2020, 143, 106825.
  18. Xiang Li, Wei Zhang, Hui Ma, Zhong Luo, Xu Li*, “Data Alignments in Machinery Remaining Useful Life Prediction Using Deep Adversarial Neural Networks”, Knowledge-Based Systems, 2020, 197, 105843.
  19. Xiang Li*, Wei Zhang, and Qian Ding, “Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks”, IEEE Transactions on Industrial Electronics, 2019, 66:7, 5525-5534. [ESI高被引论文、热点论文 ESI highly cited and hot paper]
  20. Xiang Li*, Wei Zhang, and Qian Ding, “Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction”, Reliability Engineering & System Safety, 2019, 182, 208-218. [ESI高被引论文、热点论文 ESI highly cited and hot paper]
  21. Xiang Li*, Wei Zhang, Qian Ding, and Jian-Qiao Sun, “Multi-Layer domain adaptation method for rolling bearing fault diagnosis”, Signal Processing, 2019, 157, 180-197. [ESI高被引论文、热点论文 ESI highly cited and hot paper]
  22. Wei Zhang, Xiang Li*, and Qian Ding, “Deep residual learning-based fault diagnosis method for rotating machinery”, ISA Transactions, 2019, 95, 295-305. [ESI高被引论文 ESI highly cited paper]
  23. Xiang Li*, Wei Zhang, and Qian Ding, “Understanding and Improving Deep Learning-Based Rolling Bearing Fault Diagnosis with Attention Mechanism”, Signal Processing, 2019, 161, 136-154. [ESI高被引论文 ESI highly cited paper]
  24. Xiang Li*, and Jian-Qiao Sun, “Multi-objective Optimal Predictive Control of Signals in Urban Traffic Network”, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2019, 23:4, 370-388.
  25. Xiang Li*, Qian Ding, and Jian-Qiao Sun, “Remaining useful life estimation in prognostics using deep convolution neural networks”, Reliability Engineering & System Safety, 2018, 172, 1-11. [ESI高被引论文 ESI highly cited paper]
  26. Xiang Li*, Wei Zhang, and Qian Ding, “A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning”, Neurocomputing, 2018, 310, 77-95. [ESI高被引论文 ESI highly cited paper]
  27. Xiang Li, and Jian-Qiao Sun*, “Signal Multiobjective Optimization for Urban Traffic Network”, IEEE Transactions on Intelligent Transportation Systems, 2018, 19:11, 3529-3537.

代表性会议论文 Selected Conference Publications

  1. Xiang Li*, Shahin Siahpour, Jay Lee, Yachao Wang and Jing Shi, “Deep Learning-Based Intelligent Process Monitoring of Directed Energy Deposition in Additive Manufacturing with Thermal Images”, Procedia Manufacturing, 48: 643-649, 48th North American Manufacturing Research Conference (NAMRC), Cincinnati, OH, US, 2020.
  2. Shaojie Yang, Xiang Li*, Xiaodong Jia, Yinglu Wang, Haodong Zhao and Jay Lee, “Deep Learning-Based Intelligent Defect Detection of Cutting Wheels with Industrial Images in Manufacturing”, Procedia Manufacturing, 48: 902-907, 48th North American Manufacturing Research Conference (NAMRC), Cincinnati, OH, US, 2020.
  3. Abhijeet Ainapure, Xiang Li*, Jaskaran Singh, Qibo Yang and Jay Lee, “Deep Learning-Based Cross-Machine Health Identification Method for Vacuum Pumps with Domain Adaptation”, Procedia Manufacturing, 48: 1088-1093, 48th North American Manufacturing Research Conference (NAMRC), Cincinnati, OH, US, 2020.
  4. Xiang Li, and Jian-Qiao Sun*, “Multi-Objective Optimization at Isolated Intersections with Cellular Automata”, Proceedings of Transportation Research Board 95th Annual Meeting, Washington D.C., US, 2016, 16-0675.
  5. Xiang Li, and Jian-Qiao Sun*, “Multi-objective Optimal Control Design for Adaptive Cruise Control”, Proceedings of the 8th European Nonlinear Dynamics Conference, Vienna, Austria, 2014.

专著/章节 Monograph/Chapter

Xiang Li and Jian-Qiao Sun*, “Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic”, NEO 2016. Studies in Computational Intelligence, vol 731 (Springer, 2018), Maldonado Y., Trujillo L., Schütze O., Riccardi A., Vasile M. eds.