Te Han
Impact in
- Control and Systems Engineering top 0.2%
- Machine Fault Diagnosis Techniques
- Fault Detection and Control Systems
- Mechanical Engineering top 1%
- Gear and Bearing Dynamics Analysis
- Non-Destructive Testing Techniques
Papers in
-
- Machine Fault Diagnosis Techniques 51
- Fault Detection and Control Systems 34
-
- Non-Destructive Testing Techniques 11
- Gear and Bearing Dynamics Analysis 11
- Co-authors
- Dongxiang Jiang (21 shared papers)Wenguang Yang (5 shared papers)Yan‐Fu Li (3 shared papers)Chao Liu (1 shared paper)Taotao Zhou (4 shared papers)Jiachi Yao (5 shared papers)Huixing Meng (2 shared papers)Chao Liu (4 shared papers)
- Journals
- Reliability Engineering & System Safety (7 papers)Energy (5 papers)IEEE Transactions on Instrumentation and Measurement (5 papers)IEEE Sensors Journal (4 papers)Measurement (3 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Te Han
88 papers receiving 4.6k citations
Te Han's Hit Papers
Peers
Comparison fields: 5 of 138
- Control and Systems Engineering 3.2k
- Mechanical Engineering 1.7k
- Automotive Engineering 494
- Mechanics of Materials 940
- Industrial and Manufacturing Engineering 326
Countries citing papers authored by Te Han
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
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-authors
The 25 scholars most cited alongside Te Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 92 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application Hit paper breakdown → | 2019 | 481 |
| 2 | A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults Hit paper breakdown → | 2018 | 417 |
| 3 | Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery Hit paper breakdown → | 2017 | 286 |
| 4 | A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions Hit paper breakdown → | 2021 | 217 |
| 5 | Deep transfer learning with limited data for machinery fault diagnosis Hit paper breakdown → | 2021 | 211 |
| 6 | Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles Hit paper breakdown → | 2022 | 195 |
| 7 | Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis Hit paper breakdown → | 2023 | 188 |
| 8 | Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework Hit paper breakdown → | 2022 | 187 |
| 9 | Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer Hit paper breakdown → | 2023 | 171 |
| 10 | 2018 | 171 | |
| 11 | Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data Hit paper breakdown → | 2023 | 157 |
| 12 | Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine Hit paper breakdown → | 2023 | 153 |
| 13 | 2019 | 149 | |
| 14 | 2021 | 110 | |
| 15 | 2022 | 103 | |
| 16 | 2021 | 79 | |
| 17 | 2021 | 78 | |
| 18 | 2023 | 76 | |
| 19 | 2018 | 75 | |
| 20 | 2023 | 69 |
About Te Han
Te Han is a scholar working on Control and Systems Engineering, Mechanical Engineering, Artificial Intelligence, Electrical and Electronic Engineering and Automotive Engineering, having authored 92 papers that have together received 4.7k indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (51 papers), Fault Detection and Control Systems (34 papers), Non-Destructive Testing Techniques (11 papers), Gear and Bearing Dynamics Analysis (11 papers), Advanced Battery Technologies Research (10 papers), Industrial Vision Systems and Defect Detection (9 papers), Anomaly Detection Techniques and Applications (8 papers) and Engineering Diagnostics and Reliability (8 papers). The work is most often cited by research in Control and Systems Engineering (3.2k citations), Mechanical Engineering (1.7k citations), Automotive Engineering (494 citations), Mechanics of Materials (940 citations) and Industrial and Manufacturing Engineering (326 citations). Te Han has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Dongxiang Jiang, Wenguang Yang, Yan‐Fu Li, Chao Liu, Chao Liu, Taotao Zhou, Jiachi Yao, Huixing Meng, Chao Liu and Min Qian. Their work appears in journals such as Reliability Engineering & System Safety, Energy, IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal and Measurement.
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.