张天赐,1995年生,工学博士,中南大学机电工程学院讲师。2018年获西安交通大学工学学士学位,2023年获西安交通大学工学博士学位,2022-2023年于新加坡国立大学访学。曾获陕西高等学校科学技术研究优秀成果一等奖、博士生国家奖学金、中国光谷奖学金等荣誉。
研究方向为复杂装备(航空航天发动机、大型掘进机、人形机器人等)的健康管理与智能运维,深度参与国家自然科学基金、国家重点研发计划等项目7项。在IEEE TII、IEEE TIE、MSSP等国际著名学术期刊发表研究论文20篇(ESI高被引论文3篇),论文被引总次数1200+,单篇最高被引次数360+,H-index为15,公开授权国家发明专利1项。担任IEEE TIE、MSSP、RESS等国际著名学术期刊审稿人。目前在唐进元教授科研团队(航空动力传动极端服役性能精准设计制造研究团队)从事教学科研工作。
欢迎对机械信号处理、智能故障诊断、机器学习与深度学习等方向感兴趣的同学加入课题组,来信必复!
学术研究
代表性成果
1. (SCI,JCR一区,IF=11.648)Zhang T, et al. Domain Discrepancy-guided Contrastive Feature Learning for Few-shot Industrial Fault Diagnosis under Variable Working Conditions [J]. IEEE Transactions on Industrial Informatics, 2023.
2. (SCI, JCR一区, IF=8.162)Zhang T, et al. Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines [J]. IEEE Transactions on Industrial Electronics, 2022.
3. (SCI, JCR一区, IF=8.162)Zhang T, et al. A Small Sample Focused Intelligent Fault Diagnosis Scheme of Machines via Multi-modules Learning with Gradient Penalized Generative Adversarial Networks[J]. IEEE Transactions on Industrial Electronics, 2021.
4. (SCI, JCR一区, IF=8.4)Zhang T, et al. Feature-level Consistency Regularized Semi-supervised Scheme with Data Augmentation for Intelligent Fault Diagnosis under Small Samples [J]. Mechanical Systems and Signal Processing, 2023.
5. (SCI, JCR一区, IF=5.911, ESI热点)Zhang T, et al. Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions[J]. ISA Transactions, 2022.
6. (SCI, JCR一区, IF=5.332)Zhang T, et al. Toward Small Sample Challenge in Intelligent Fault Diagnosis: Attention-Weighted Multidepth Feature Fusion Net with Signals Augmentation [J]. IEEE Transactions on Instrumentation and Measurement, 2022.
7. (SCI, JCR一区, IF=5.332)Zhang T, et al. SASLN: Signals Augmented Self-Taught Learning Networks for Mechanical Fault Diagnosis Under Small Sample Condition [J]. IEEE Transactions on Instrumentation and Measurement, 2021.
8. 陈景龙, 张天赐, 訾艳阳. 小样本下基于自我学习的变工况机械故障智能诊断方法, CN110647923A[P]. 2020.