Te Han

 

Shuimu Tsinghua Scholar, Ph.D.
Postdoctoral Research Fellow

 

Office: Shuangqing Building, #4-1106
Department of Industrial Engineering,
Tsinghua University,
Beijing, 100084, China
Tel: +86-18511860565
E-mail: hant15@tsinghua.org.cn; hant@mail.tsinghua.edu.cn

[Profile] [Education Background] [Academic Experience] [Research Interests] [Honour] [Selected Publications] [Academic Service]

Profile:

Dr. Han received the B.S. and Ph.D. degree in Energy and Power Engineering from Tsinghua University, Beijing, P.R. China, in 2015 and 2020, respectively. In 2019, he was a visiting scholar in University of Alberta, Canada. He is currently a Postdoctoral Research Fellow with Department of Industrial Engineering, Tsinghua University. His research interests include machinery condition monitoring, intelligent fault diagnosis and prognostics, and industrial big data analysis.

He has authored and coauthored more than 30 articles in technical journals and conference proceedings. Four articles are ranked as "Top 1% ESI Highly Cited Papers", and one article is ranked as "Top 0.1% ESI Hot Papers" in web of science. He was honored with the "Shuimu Tsinghua Scholar", Excellent Doctoral Dissertation of Tsinghua University, National Scholarship for Doctoral Students of the Ministry of Education of China, Outstanding reviewer status for ISA Transactions.

Education Background:

 

Academic Experience:

Research Interests:

 

Honour:

 

Selected Publications:

Journal publications:

    1. 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, doi: 10.1109/TIM.2021.3088489.
    2. 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.
    3. 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, doi: 10.1109/JSEN.2021.3126864.
    4. 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.
    5. 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 1% ESI Highly Cited Papers, Top 1% ESI Highly Cited Papers, The most cited articles published since 2018 in ISA Transactions)
    6. 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 Highly Cited Papers)
    7. 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 Highly Cited Papers, The most cited articles published since 2018 in Knowledge-Based Systems)
    8. 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. (The most cited articles published since 2018 in ISA Transactions)
    9. 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.
    10. 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 Highly Cited Papers, The most cited articles in the last 3 years of Transactions of the Institute of Measurement and Control)

Conference proceedings:

    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, July 07-11.
    2. Han Te, Liu Chao*, Yang Wenguang, Jiang Dongxiang. Convolutional neural network-based transfer learning method for mechanical fault diagnosis. 2018 National Equipment Monitoring, Diagnosis and Maintenance Conference, Baotou, Inner Mongolia, 2018, Aug 10-12. (Excellent Paper Award)
    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 July 02-05.
    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, July 18-20.
    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 May 22-24.
    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 May 24-26.

Academic Service: