Te Han

5.7k total citations · 11 hit papers
90 papers, 4.4k citations indexed

About

Te Han is a scholar working on Control and Systems Engineering, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Te Han has authored 90 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Control and Systems Engineering, 28 papers in Mechanical Engineering and 17 papers in Artificial Intelligence. Recurrent topics in Te Han's work include Machine Fault Diagnosis Techniques (50 papers), Fault Detection and Control Systems (34 papers) and Gear and Bearing Dynamics Analysis (11 papers). Te Han is often cited by papers focused on Machine Fault Diagnosis Techniques (50 papers), Fault Detection and Control Systems (34 papers) and Gear and Bearing Dynamics Analysis (11 papers). Te Han collaborates with scholars based in China, Hong Kong and United States. Te Han's co-authors include Dongxiang Jiang, Wenguang Yang, Yan‐Fu Li, Chao Liu, Chao Liu, Taotao Zhou, Huixing Meng, Chao Liu, Jiachi Yao and Min Qian and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Journal of Power Sources and Applied Energy.

In The Last Decade

Te Han

83 papers receiving 4.3k citations

Hit Papers

Deep transfer network wit... 2017 2026 2020 2023 2019 2018 2017 2021 2021 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application
    2019 468 citations Te Han et al. profile →
  2. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
    2018 402 citations Te Han et al. Knowledge-Based Systems profile →
  3. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery
    2017 267 citations Te Han et al. profile →
  4. A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions
    2021 209 citations Te Han et al. profile →
  5. Deep transfer learning with limited data for machinery fault diagnosis
    2021 196 citations Te Han et al. profile →
  6. Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
    2022 182 citations Te Han et al. Reliability Engineering & System Safety profile →
  7. Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis
    2023 178 citations Huixing Meng, Te Han et al. Reliability Engineering & System Safety profile →
  8. Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
    2022 172 citations Te Han et al. Reliability Engineering & System Safety profile →
  9. Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer
    2023 155 citations Haidong Shao, Te Han et al. profile →
  10. Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data
    2023 148 citations Jiachi Yao, Te Han Energy profile →
  11. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
    2023 145 citations Te Han, Zhongyi Pei et al. Information Sciences profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Te Han 3.1k 1.7k 917 826 604 90 4.4k
Xingxing Jiang 3.4k 1.1× 2.1k 1.2× 1.1k 1.2× 477 0.6× 496 0.8× 144 4.5k
V. Sugumaran 2.3k 0.8× 1.9k 1.1× 828 0.9× 534 0.6× 596 1.0× 184 4.3k
Zhibin Zhao 4.0k 1.3× 2.2k 1.3× 1.1k 1.2× 1.3k 1.6× 913 1.5× 148 6.3k
Achmad Widodo 2.2k 0.7× 1.2k 0.7× 676 0.7× 390 0.5× 556 0.9× 106 3.4k
Clarence W. de Silva 2.1k 0.7× 1.3k 0.7× 493 0.5× 731 0.9× 823 1.4× 214 4.6k
Min Xia 2.5k 0.8× 1.6k 0.9× 886 1.0× 657 0.8× 643 1.1× 112 4.4k
Bo‐Suk Yang 3.8k 1.3× 2.0k 1.2× 1.2k 1.3× 676 0.8× 800 1.3× 109 5.9k
Dongxiang Jiang 3.1k 1.0× 2.0k 1.2× 947 1.0× 591 0.7× 581 1.0× 124 4.4k
Shaohui Zhang 1.8k 0.6× 920 0.5× 438 0.5× 514 0.6× 785 1.3× 93 3.0k

Countries citing papers authored by Te Han

Since Specialization
Citations

This map shows the geographic impact of Te Han's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Te Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Te Han more than expected).

Fields of papers citing papers by Te Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Te Han. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Te Han. The network helps show where Te Han may publish in the future.

Co-authorship network of co-authors of Te Han

This figure shows the co-authorship network connecting the top 25 collaborators of Te Han. A scholar is included among the top collaborators of Te Han based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Te Han. Te Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Han, Juntao, et al.. (2025). Data-driven distributionally robust optimization of low-carbon data center energy systems considering multi-task response and renewable energy uncertainty. Journal of Building Engineering. 102. 111937–111937. 6 indexed citations
2.
3.
Han, Te, et al.. (2025). Designing and regulating clean energy data centres. 1(6). 373–374. 3 indexed citations
4.
Zhang, Jie, Yun Kong, Zhuyun Chen, et al.. (2024). CBAM-CRLSGAN: A novel fault diagnosis method for planetary transmission systems under small samples scenarios. Measurement. 234. 114795–114795. 20 indexed citations
5.
Han, Te, et al.. (2024). Towards more reliable photovoltaic energy conversion systems: A weakly-supervised learning perspective on anomaly detection. Energy Conversion and Management. 316. 118845–118845. 40 indexed citations
6.
Han, Te, et al.. (2024). Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives. Renewable and Sustainable Energy Reviews. 205. 114861–114861. 15 indexed citations
7.
Yao, Yuantao, Te Han, Jie Yu, & Min Xie. (2024). Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems. Energy. 291. 130419–130419. 40 indexed citations
8.
Liu, Ruonan, Te Han, Boyuan Yang, et al.. (2024). Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems. 2. 264–280. 8 indexed citations
9.
Wang, Zuolu, et al.. (2024). A Meta-Learning Method for Few-Shot Multidomain State-of-Health Estimation of Lithium-Ion Batteries. IEEE Transactions on Transportation Electrification. 11(1). 4830–4840. 6 indexed citations
10.
Wang, Huan, et al.. (2024). Dynamic Subdomain Pseudolabel Correction and Adaptation Framework for Multiscenario Mechanical Fault Diagnosis. IEEE Transactions on Reliability. 74(1). 2421–2433. 4 indexed citations
11.
Yao, Jiachi, et al.. (2024). Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems. Energy. 294. 130882–130882. 50 indexed citations
12.
Tian, Jinpeng, Liang Ma, Tieling Zhang, et al.. (2024). Exploiting domain knowledge to reduce data requirements for battery health monitoring. Energy storage materials. 67. 103270–103270. 32 indexed citations
13.
Yao, Jiachi & Te Han. (2023). Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data. Energy. 271. 127033–127033. 148 indexed citations breakdown →
14.
Han, Te, et al.. (2023). Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine. Information Sciences. 648. 119496–119496. 145 indexed citations breakdown →
15.
Han, Te, et al.. (2023). A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems. Engineering Applications of Artificial Intelligence. 125. 106707–106707. 49 indexed citations
16.
Wang, Zhe, Zhiying Wu, Xingqiu Li, et al.. (2023). Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis. Knowledge-Based Systems. 278. 110891–110891. 64 indexed citations
17.
Han, Te, Jinpeng Tian, C. Y. Chung, & Yi‐Ming Wei. (2023). Challenges and opportunities for battery health estimation: Bridging laboratory research and real-world applications. Journal of Energy Chemistry. 89. 434–436. 68 indexed citations
18.
Meng, Huixing, et al.. (2023). 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. 236. 109288–109288. 178 indexed citations breakdown →
19.
Wang, Zhe, et al.. (2022). Health Monitoring of Plate Structures Based on Tomography With Combination of Guided Wave Transmission and Reflection. IEEE Sensors Journal. 22(11). 10850–10860. 6 indexed citations
20.
Han, Te, Zhe Wang, & Huixing Meng. (2021). End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation. Journal of Power Sources. 520. 230823–230823. 106 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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