Suivre
Te Han / 韩特
Te Han / 韩特
Autres nomsT. Han, Han Te, Han T.
Associate Professor, Beijing Institute of Technology, China
Adresse e-mail validée de bit.edu.cn - Page d'accueil
Titre
Citée par
Citée par
Année
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
5072020
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
4572019
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
3372018
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
2082021
Deep transfer learning with limited data for machinery fault diagnosis
T Han, C Liu, R Wu, D Jiang
Applied Soft Computing 103, 107150, 2021
1862021
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
T Zhou, T Han, EL Droguett
Reliability Engineering & System Safety 224, 108525, 2022
1802022
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
1772019
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
1712019
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
1612022
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
1382023
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
T Han, W Xie, Z Pei
Information Sciences 648, 119496, 2023
1282023
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
1272023
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
1202023
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
1062022
A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network
T Zhou, S Jiang, T Han, SP Zhu, Y Cai
International Journal of Fatigue 166, 107234, 2023
862023
Weighted domain adaptation networks for machinery fault diagnosis
D Wei, T Han, F Chu, MJ Zuo
Mechanical Systems and Signal Processing 158, 107744, 2021
842021
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
772018
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
762021
Rolling Bearing Fault Diagnostic Method Based on VMD‐AR Model and Random Forest Classifier
T Han, D Jiang
Shock and Vibration 2016 (1), 5132046, 2016
592016
Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis
Y Miao, C Li, H Shi, T Han
Mechanical Systems and Signal Processing 189, 110110, 2023
562023
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