Email: hante@bit.edu.cn, hant15@tsinghua.org.cn

BIOGRAPHY

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Dr. Han’s research interests include sustainable energy, prognostics and health management, smart sensors, as well as machine learning. He has authored and co-authored two books and more than 50 articles in technical journals and conference proceedings. Ten of his articles have been honored with the "ESI highly cited paper", and three articles has been honored with the "ESI hot paper" in the Web of Science. He has been recognized as one of the World’s Top 2% Scientists by Stanford University consecutively from 2020 to 2022. He is an associate editor of “IEEE Sensors Journal”. In addition, he has previously served as a guest editor for internationally renowned journals such as “Measurement Science and Technology”, “IEEE Transactions on Industrial Cyber-Physical Systems”, “Reliability Engineering & System Safety”, etc. He is also an active reviewer for over 60 prestigious journals from Elsevier, IEEE, SAGE, Springer and other publishers and several conferences. He was awarded the Outstanding Ph.D. Graduate Thesis by Tsinghua University and selected as one of the "Shuimu Scholars" in Tsinghua University.

EDUCATION

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Ph.D.                Energy and Power Engineering              Tsinghua University     8/2015 - 7/2020

Visiting Ph.D.   Mechanical Engineering                          University of Alberta    3/2019 - 9/2019

B.S.                  Energy and Power Engineering              Tsinghua University     8/2011 - 6/2015

APPOINTMENTS

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Research Associate         Industrial Engineering              Tsinghua University                   9/2020 - 2/2023

Associate Professor         Management Engineering       Beijing Institute of Technology   3/2023-present

PUBLICATIONS

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* means the corresponding author.

Refereed Journal Papers

[1] Han Te, Xie Wenzhen*, Pei Zhongyi. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine. Information Sciences, 2023, 648, 119496. (JCR Q1, IF= 8.1)

[2] Han Te, Tian Jinpeng*, Chung C.Y.*, Wei Yi-Ming. Challenges and opportunities for battery health estimation: Bridging laboratory research and real-world applications. Journal of Energy Chemistry, 2023. (JCR Q1, IF= 13.1)

[3] Yao Jiachi and Han Te*. Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data. Energy, 2023, 271, 127033. (JCR Q1, IF= 9.0, ESI Top 1% Highly Cited Paper)

[4] Xie Wenzhen, Han Te*, Pei Zhongyi and Xie Min. A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems. Engineering Applications of Artificial Intelligence, 2023, 125, 106707. (JCR Q1, IF= 8.0)

[5] Wang Zhe, Wu Zhiying, Li Xingqiu, Shao Haidong, Han Te*, Xie Min. Attention-aware temporal-spatial graph neural network with multi-sensor information fusion for fault diagnosis. Knowledge-Based Systems, 2023, 278, 110891. (JCR Q1, IF= 8.8)

[6] Miao Yonghao, Li Chenhui, Shi Huifang and Han Te*. Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis. Mechanical Systems and Signal Processing, 2023, 189: 110110. (JCR Q1, IF= 8.4)

[7] Meng Huixing, Geng Mengyao and Han Te*. Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis. Reliability Engineering & System Safety, 2023, 236, 109288. (JCR Q1, IF= 8.1)

[8] Zhou Taotao, Jiang Shan*, Han Te, Zhu Shun-Peng, Cai Yinan. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network. International Journal of Fatigue, 2023, 166, 107234. (JCR Q1, IF= 6.0, ESI Top 1% Highly Cited Paper)

[9] 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. (JCR Q1, IF= 8.1, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)

[10] 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. (JCR Q1, IF= 8.1, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)

[11] 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. (JCR Q1, IF= 9.2)

[12] 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. (JCR Q1, IF=5.4)

[13] 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. (JCR Q1, IF= 5.6, ESI Top 1% Highly Cited Paper)

[14] 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. (JCR Q1, IF= 8.7, ESI Top 1% Highly Cited Paper)

[15] Wei Dongdong, Han Te, Chu F.L. and Zuo M.J.*. Weighted domain adaptation networks for machinery fault diagnosis. Mechanical Systems and Signal Processing, 2021, 158: 107744. (JCR Q1, IF=8.4)

[16] Qian Min, Li Yan-Fu*, Han Te. Positive-unlabeled learning-based hybrid deep network for intelligent fault detection. IEEE Transactions on Industrial Informatics, 2021, 18(7): 4510-4519. (JCR Q1, IF=12.3)

[17] 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. (JCR Q1, IF= 7.3, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)

[18] 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. (JCR Q1, IF= 8.8, ESI Top 1% Highly Cited Paper)

[19] 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. (JCR Q1, IF= 8.4, ESI Top 1% Highly Cited Paper)

[20] 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. (JCR Q1, IF= 7.3)

[21] 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. (JCR Q2, IF=2.7)

[22] 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. (JCR Q1, IF= 5.6)

[23] 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. (JCR Q3, IF= 1.8, ESI Top 1% Highly Cited Paper)

[24] Han Te, Jiang Dongxiang*, Zhang Xiaochen and Sun Yankui. Intelligent diagnosis method for rotating machinery using dictionary learning and singular value decomposition. Sensors, 2017, 17(4): 689. (JCR Q2, IF= 3.9)

[25] 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. (JCR Q1, IF= 4.3)

[26] 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. (JCR Q1, IF= 4.3)

[27] 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. (JCR Q1, IF= 4.3)

[28] Pei Shicheng, Wang Huan, Han Te. Time-efficient neural architecture search for autonomous underwater vehicle fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-11. (JCR Q1, IF= 5.6)

[29] Zhang Xiaochen, Luo Tianjian*, Han Te and Gao Hongli. A novel performance degradation prognostics approach and its application on ball screw. Measurement, 2022, 195: 111184. (JCR Q1, IF=5.6)

[1] Wu Rui, Liu Chao, Han Te, Yao Jiachi and Jiang Dongxiang. A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model. Measurement Science and Technology, 2022, 34(2): 024006. (JCR Q1, IF=2.4)

Refereed Conference Papers

[1] Deng Zhenyu, Han Te*, Liu Ruonan and Zhi Fengyao. A fault diagnosis method in industrial processes with integrated feature space and optimized random forest. 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, AK, USA, 2022, pp. 1170-1173.

[2] Xie Wenzhen, Han Te* and Shao Haidong. Unsupervised domain adaptation for bearing fault diagnosis using nonlinear impact dynamics model under limited supervision. 2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), Harbin, China, 2022, pp. 1-5.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

PROFESSIONAL ACTIVITIES

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Editorial Positions and Conference Committees

l Associate Editor, IEEE Sensors Journal, 2023 to present.

l Editorial Board Member, Journal of Dynamics, Monitoring and Diagnostics, 2022 to present.

l Leading Guest Editor, Reliability Engineering & System Safety, Special Issue on Scientific Machine Learning for Enhancing Reliability and Safety of AI-powered Systems (2023).

l Guest Editor, IEEE Transactions on Industrial Cyber-Physical Systems, Special Issue on Machine Learning for Prognostics and Health Management of Industrial Cyber-physical Systems (2023).

l Guest Editor, Journal of Risk and Reliability, Special Issue on Domain-Knowledge Guided Machine Learning in Safety-Critical Applications (2022).

l Guest Editor, Measurement Science and Technology, Special Issue on AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management (2022).

l Leading Guest Editor, Machines, Special Issue on Fault Diagnosis and Health Management of Power Machinery (2022).

l Session Chair, The 2023 IEEE Global Reliability & Prognostics and Health Management Conference (IEEE GlobalRel & PHM 2023)

l Session Chair, The 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT 2023)

l Session Organizer, The Fourth International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2023)

l Organizing Committee, The International Conference on Aerospace Structural Dynamics (ICASD 2023)

l Session Organizer, The Third International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2022)

Referees for Journals and Conferences

Reviewer for over 60 prestigious journals, such as

l ISA Transactions (Outstanding reviewer status award, Top 10%)

l IEEE Sensors Journal

l Measurement

l Measurement Science and Technology

l Transactions of the Institute of Measurement and Control

l IEEE Transactions on Systems, Man, and Cybernetics: Systems

l IEEE Transactions on Cybernetics

l IEEE Transactions on Industrial Electronics

l IEEE Transactions on Industrial Informatics

l IEEE Transactions on Instrumentation and Measurement

l IEEE Transactions on Reliability

l IEEE/ASME Transactions on Mechatronics

l IEEE Transactions on Automation Science and Engineering

l IEEE Transactions on Intelligent Transportation Systems

l IEEE Transactions on Neural Networks and Learning Systems

l IEEE Internet of Things Journal

l Mechanical Systems and Signal Processing

l Knowledge-Based Systems

l Applied Mathematical Modelling

l Neurocomputing

l Automatika

l International Journal of Production Research

l IET Signal Processing

l IET Generation, Transmission & Distribution

l Journal of Risk and Reliability

l Journal of Mechanical Engineering Science

l Journal of Process Mechanical Engineering

AWARDS AND HONORS

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l 2023, World’s Top 2% Scientists by Stanford University

l 2023, Journal of Dynamics, Monitoring and Diagnostics Outstanding Member of the Youth Editorial Board

l 2022, World’s Top 2% Scientists by Stanford University

l 2021, World’s Top 2% Scientists by Stanford University

l 2020, Selected for the "Shuimu Scholars" at Tsinghua University

l 2020, Outstanding Ph.D. Graduate Thesis of Tsinghua University (Top 5% students)

l 2020, World’s Top 2% Scientists by Stanford University

l 2019, National Scholarship for Doctoral Students, Ministry of Education (Top 10% students)

l 2019, ISA Transactions, Outstanding Reviewer Status Award (Top 10%)

l 2018, Outstanding Paper Award in Equipment Monitoring and Diagnosis and Maintenance Conference

l 2018, 1^st People’s Scholarship of Tsinghua University (Top 5% students)

l 2017, 2^nd People’s Scholarship of Tsinghua University (Top 10% students)

l 2016, Mitsubishi Heavy Industries Scholarship

PROJECT EXPERIENCES

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l Principle Investigator. Research on Health State Characterization and Intelligent Domain Generalization Diagnosis for Wind Turbine Systems Under Limited Data, National Natural Science Foundation of China, 300,000 RMB

l Principle Investigator. Research on Data-driven Intelligent Fault Diagnosis and Root Cause Analysis Method for High-speed Train Transmission System, China Postdoctoral Science Foundation-Special Fund, 180,000 RMB

l Principle Investigator. Intelligent Transfer Fault Diagnosis and Root Cause Analysis for High-speed Train Transmission System, China Postdoctoral Science Foundation-General Fund, 80,000 RMB

l Participant. Intelligent Fault Diagnosis and Health Assessment System for Industrial Robots, National Key R&D Program Projects of China, 13 million RMB

l Participant. Establishment of Key Technical Standards and Detection System for Gas Turbine Air Quality Assurance, National Key R&D Program Projects of China, 21 million RMB

l Participant. Study on Strength and Fatigue Life of Middle and Low Pressure Welded Rotor of Combined Cycle Turbine, Shanghai Electric Power Generation Equipment Co., Ltd., 380,000 RMB

l Participant. CJ-1000AX Vibration Test and Health Management System, AECC Commercial Aircraft Engine Co., Ltd., 450,000 RMB

l Participant. Research on Vibration Fault Diagnosis Technology for Commercial Aeroengine, AECC Commercial Aircraft Engine Co., Ltd., 560,000 RMB

PRESENTATIONS, TALKS AND DISCUSSIONS

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l Workshop in Intelligent Maintenance and Health Management for Large Equipment, 30 Oct., 2021, Organized by Tianjin University

Title: AI-based Machinery fault diagnosis

Invited Presenter: Dr. Te Han

l Workshop in Development of Intelligent Manufacturing Equipment for Lithium Battery, 6 Nov., 2020, Organized by GEESUN Co., Ltd.

Title: Prognosis and Health Management of Lithium Battery

Invited Presenter: Dr. Te Han

Updated in Nov., 2023