| 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 | 320 | 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 | 305 | 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 | 209 | 2018 |
| 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 | 124 | 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 | 106 | 2019 |
| 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 | 86 | 2021 |
| Deep transfer learning with limited data for machinery fault diagnosis T Han, C Liu, R Wu, D Jiang Applied Soft Computing 103, 107150, 2021 | 76 | 2021 |
| 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 | 55 | 2018 |
| Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier T Han, D Jiang Shock and Vibration 2016, 2016 | 47 | 2016 |
| Weighted domain adaptation networks for machinery fault diagnosis D Wei, T Han, F Chu, MJ Zuo Mechanical Systems and Signal Processing 158, 107744, 2021 | 40 | 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 | 38 | 2016 |
| Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework T Zhou, T Han, EL Droguett Reliability Engineering & System Safety 224, 108525, 2022 | 37 | 2022 |
| 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 | 37 | 2017 |
| Intelligent diagnosis method for rotating machinery using dictionary learning and singular value decomposition T Han, D Jiang, X Zhang, Y Sun Sensors 17 (4), 689, 2017 | 33 | 2017 |
| 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 | 28 | 2022 |
| Dynamic characteristics and experimental research of dual-rotor system with rub-impact fault H Xu, N Wang, D Jiang, T Han, D Li Shock and Vibration 2016, 2016 | 28 | 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 | 23 | 2021 |
| Dynamic characteristics of rotor system and rub-impact fault feature research based on casing acceleration NF Wang, DX Jiang, T Han Journal of Vibroengineering 18 (3), 1525-1539, 2016 | 21 | 2016 |
| 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 | 15 | 2022 |
| Rotating machinery fault diagnosis for imbalanced data based on decision tree and fast clustering algorithm X Zhang, D Jiang, Q Long, T Han Journal of Vibroengineering 19 (6), 4247-4259, 2017 | 15 | 2017 |
Chao LiuAssociate Professor, Department of Energy and Power Engineering, Tsinghua University, China在 tsinghua.edu.cn 的电子邮件经过验证
