Te Han / 韩特

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Chao LiuAssociate Professor, Department of Energy and Power Engineering, Tsinghua University, ChinaVerified email at tsinghua.edu.cn
Yan-Fu LiProfessor, Tsinghua University, ChinaVerified email at tsinghua.edu.cn
Haidong ShaoAssociate Professor, College of Mechanical and Vehicle Engineering, Hunan University,Verified email at hnu.edu.cn
Taotao ZhouChina University of Petroleum-BeijingVerified email at cup.edu.cn
Enrique Lopez DroguettUniversity of California, Los AngelesVerified email at ucla.edu
Soumik SarkarDirector, Translational AI Center, Professor, Iowa State UniversityVerified email at iastate.edu
Zhongyi PeiAssistant Researcher, Tsinghua UniversityVerified email at tsinghua.edu.cn
Linjiang WuSr. System EngineerVerified email at continental-corporation.com
Ming J ZuoUniversity of AlbertaVerified email at ualberta.ca
Min XieChair Professor of Industrial Engineering, City University of Hong KongVerified email at cityu.edu.hk
Bin LiuUniversity of StrathclydeVerified email at strath.ac.uk
Jinpeng TianThe Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk
Shun-Peng Zhu (朱顺鹏)Professor, University of Electronic Science and Technology of ChinaVerified email at uestc.edu.cn
Chi Yung ChungUniversity of SaskatchewanVerified email at usask.ca
A. MOSLEHUniversity of California Los Angeles (UCLA)Verified email at ucla.edu
PRADEEP KUNDUAssistant Professor, KU LeuvenVerified email at kuleuven.be
Konstantinos C. GrylliasAssociate Professor, Department of Mechanical Engineering, KULeuven, BelgiumVerified email at kuleuven.be
Huan Wang / 王欢Department of Industrial Engineering, Tsinghua UniversityVerified email at mails.tsinghua.edu.cn
Zhonghao Chang / 常中豪Beijing Institute of Technology, ChinaVerified email at bit.edu.cn

Te Han / 韩特

Other namesT. Han, Han Te, Han T.

Associate Professor, Beijing Institute of Technology, China

Verified email at bit.edu.cn

Sustainable Energy Machine Learning Industrial Diagnostics Prognostics and Health Management


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application T Han, C Liu, W Yang, D Jiang ISA transactions 97, 269-281, 2020	438	2020
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults T Han, C Liu, W Yang, D Jiang Knowledge-based systems 165, 474-487, 2019	395	2019
Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery T Han, D Jiang, Q Zhao, L Wang, K Yin Transactions of the Institute of Measurement and Control 40 (8), 2681-2693, 2018	294	2018
A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions T Han, YF Li, M Qian IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021	161	2021
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems T Han, C Liu, L Wu, S Sarkar, D Jiang Mechanical Systems and Signal Processing 117, 170-187, 2019	152	2019
Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions T Han, C Liu, W Yang, D Jiang ISA transactions 93, 341-353, 2019	147	2019
Deep transfer learning with limited data for machinery fault diagnosis T Han, C Liu, R Wu, D Jiang Applied Soft Computing 103, 107150, 2021	135	2021
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles T Han, YF Li Reliability Engineering & System Safety 226, 108648, 2022	114	2022
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework T Zhou, T Han, EL Droguett Reliability Engineering & System Safety 224, 108525, 2022	113	2022
End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation T Han, Z Wang, H Meng Journal of Power Sources 520, 230823, 2022	74	2022
Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data J Yao, T Han Energy 271, 127033, 2023	73	2023
Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification T Han, D Jiang, Y Sun, N Wang, Y Yang Measurement 118, 181-193, 2018	71	2018
Weighted domain adaptation networks for machinery fault diagnosis D Wei, T Han, F Chu, MJ Zuo Mechanical Systems and Signal Processing 158, 107744, 2021	65	2021
Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis H Meng, M Geng, T Han Reliability Engineering & System Safety 236, 109288, 2023	59	2023
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in Transformer Y Xiao, H Shao, M Feng, T Han, J Wan, B Liu Journal of Manufacturing Systems 70, 186-201, 2023	58	2023
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine T Han, W Xie, Z Pei Information Sciences 648, 119496, 2023	57	2023
Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier T Han, D Jiang Shock and Vibration 2016, 2016	56	2016
Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression X Li, X Zhong, H Shao, T Han, C Shen Reliability Engineering & System Safety 216, 108018, 2021	47	2021
The fault feature extraction of rolling bearing based on EMD and difference spectrum of singular value T Han, D Jiang, N Wang Shock and vibration 2016, 2016	46	2016
Rotating machinery fault diagnosis for imbalanced data based on fast clustering algorithm and support vector machine X Zhang, D Jiang, T Han, N Wang, W Yang, Y Yang Journal of Sensors 2017, 2017	40	2017

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