韩特

 

工学博士、水木学者
工业工程系博士后

 

北京市海淀区清华大学工业工程系双清大厦4-1106
邮编：100084
电话：+86-18511860565
电子邮件: hant15@tsinghua.org.cn;hant@mail.tsinghua.edu.cn

[个人简介] [教育背景] [工作经历] [研究兴趣] [荣誉奖励] [代表性论著] [科研项目] [学术兼职与服务][English Version]

个人简介：

韩特，男，1993年生，毕业于清华大学能源与动力工程系，工学博士，水木学者。主要研究方向包括大数据驱动的装备故障诊断与智能运维、故障预测与健康管理、系统可靠性等，以期将其应用在轨道交通、风电等复杂工程系统中。在国际高水平期刊或会议上发表论文30余篇。以第一/唯一通讯作者身份发表SCI论文10余篇，入选ESI高被引论文5篇，ESI热点论文1篇（均以第一作者身份发表，均非综述性论文）。担任Elsevier、IEEE、SAGE、Springer等出版社的60余种SCI期刊同行评审专家。担任《Journal of Risk and Reliability》、《Measurement Science and Technology》、《Machines》等著名国际期刊客座编辑，组织“工业装备故障诊断与健康管理研究”相关特刊。主持国家自然科学基金1项、中国博士后科学基金特别资助1项、中国博士后科学基金面上资助1项。曾获清华大学优秀博士论文、教育部博士生国家奖学金、三菱重工奖学金等多项荣誉。

 

教育背景：

 

工作经历：

 

研究兴趣：

 

荣誉奖励：

 

代表性论著：

期刊论文：

    1. 韩特, 李彦夫*, 雷亚国, 李乃鹏, 李响. 融合图标签传播和判别特征增强的工业机器人关键部件半监督故障诊断方法. 机械工程学报, 1-9, 在线出版.
    2. 韩特, 刘超*, 沈长青, 史红梅, 司瑾, 蒋东翔. 深度嵌入度量学习的机械跨工况故障识别方法. 振动工程学报, 1-9, 在线出版.
    3. Han Te and Li Yan-Fu* . Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles. Reliability Engineering & System Safety, 2022, 226, 108648.
    4. Zhou Taotao, Han Te* and Enrique Lopez Droguett. Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework. Reliability Engineering & System Safety, 2022, 224: 108525.
    5. Han Te, Wang Zhe* and Meng Huixing. End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation. Journal of Power Sources, 2022, 520: 230823.
    6. Han Te, Zhou Taotao, Xiang Yongyong* and Jiang Dongxiang. Cross-machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer. Structural Control and Health Monitoring, 2022, 29(3): e2898
    7. Han Te, Li Yan-Fu* and Qian Min. A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3520011.(Top 1% ESI高被引论文)
    8. Han Te, Liu Chao*, Wu Rui and Jiang Dongxiang. Deep transfer learning with limited data for machinery fault diagnosis. Applied Soft Computing, 2021, 103: 107150.
    9. Han Te, Liu Chao*, Yang Wenguang and Jiang Dongxiang. Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application. ISA Transactions, 2020, 97: 269-281. (Top 0.1% ESI热点论文，Top 1% ESI高被引论文)
    10. Han Te, Liu Chao*, Wu Linjiang, Sarkar Soumik and Jiang Dongxiang. An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. Mechanical Systems and Signal Processing, 2019, 117: 170-187. (Top 1% ESI高被引论文)
    11. Han Te, Liu Chao*, Yang Wenguang and Jiang Dongxiang. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems, 2019, 165: 474-487. (Top 1% ESI高被引论文)
    12. Han Te, Liu Chao*, Yang Wenguang and Jiang Dongxiang. Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Transactions, 2019, 93: 341-353.
    13. Zhao Qi, Han Te*, Jiang Dongxiang and Yin Kai. Application of variational mode decomposition to feature isolation and diagnosis in a wind turbine. Journal of Vibration Engineering & Technologies, 2019, 7(6): 639-646.
    14. Han Te, Jiang Dongxiang*, Sun Yankui, Wang Nanfei and Yang Yizhou. Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification. Measurement, 2018, 118: 181-193.
    15. Han Te*, Jiang Dongxiang, Zhao Qi, Wang Lei and Yin Kai. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control, 2018, 40(8): 2681-2693. (Top 1% ESI高被引论文)
    16. Wang Zhe, Han Te, Wang Shen, Zhan Zaifu, Zhao Wei and Huang Songling*. Health monitoring of plate structures based on tomography with combination of guided wave transmission and reflection. IEEE Sensors Journal, 2022, 22(11): 10850-10860.
    17. Deng Zhenyu, Han Te, Zheng Hao, Zhi Fengyao, Jiang Jiajia and Duan Fajie*. Critical concurrent feature selection and enhanced heterogeneous ensemble learning approach for fault detection in industrial processes. IEEE Sensors Journal, 2022, 22(8): 7931-7943.
    18. Deng Zhenyu, Han Te,Cheng Zhonghai, Jiang Jiajia and Duan Fajie*. “Fault detection of petrochemical process based on space-time compressed matrix and Naive Bayes. Process Safety and Environmental Protection, 2022, 160): 327-340.
    19. Si Jin, Shi Hongmei*, Han Te, Chen Jingcheng and Zheng Changchang. Learn generalized features via multi-source domain adaptation: Intelligent diagnosis under variable/constant machine conditions. IEEE Sensors Journal, 2022, 22(1): 510-519.
    20. Wei Dongdong, Han Te, Chu Fulei and Zuo Ming Jian*. Weighted domain adaptation networks for machinery fault diagnosis. Mechanical Systems and Signal Processing, 2021, 158: 107744.

会议论文：

    1. Han Te, Jiang Dongxiang*. Deep learning approach considering imbalanced data for health condition monitoring in wind turbine. 26th International Congress on Sound and Vibration, Montreal, Canada, 2019-07-07 to 2019-07-11.
    2. 韩特, 刘超*, 杨文广, 蒋东翔. 基于卷积神经网络的迁移学习在机械故障诊断中应用. 2018年全国设备监测诊断与维护学术会议, 包头, 内蒙古, 2018-08-10 至 2018-08-12. (会议优秀论文)
    3. Han Te, Long Quan, Liu Chao*, and Jiang Dongxiang. A deep statistical feature learning method based on stacked auto-encoder for intelligent diagnosis of rolling bearing. 31st International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management COMADEM 2018, Sun City, South Africa, 2018-07-02 to 2018-07-05.
    4. Han Te, Jiang Dongxiang*, Yang Wenguang. Degradation state assessment of rolling bearing based on variational mode decomposition and energy distribution. International Conference on Fracture and Damage Mechanics, Florence, Italy, 2017-07-18 to 2017-07-20.
    5. Han Te, Jiang Dongxiang*. Application of variational mode decomposition to misalignment fault diagnosis in a wind turbine. Surveillance 9 International Conference, Fes, Morocco, 2017-05-22 to 2017-05-24.
    6. Han Te, Jiang Dongxiang*, Wang Nanfei. The fault simulation experiment and feature extraction of rolling bearing based on casing measuring point. 2016 Joint Conference/Symposium of the Society for Machinery Failure Prevention Technology and the International Society of Automation, Daytona, OH, 2016-05-24 to 2016-05-26.

 

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学术兼职与服务：