Yafei Deng
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- Manufacturing Process and Optimization 7
- Industrial Vision Systems and Defect Detection 3
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- Machine Fault Diagnosis Techniques 6
- Fault Detection and Control Systems 2
- Mechanical Engineering top 5%
- Advanced Measurement and Metrology Techniques 3
- Advanced machining processes and optimization 3
- Non-Destructive Testing Techniques 3
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- Mechanical stress and fatigue analysis 1
Yafei Deng
14 papers receiving 960 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Industrial and Manufacturing Engineering 221
- Control and Systems Engineering 434
- Medical Laboratory Technology 27
- Safety, Risk, Reliability and Quality 114
- Mechanical Engineering 433
Countries citing papers authored by Yafei Deng
This map shows the geographic impact of Yafei Deng'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 Yafei Deng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yafei Deng more than expected).
Fields of papers citing papers by Yafei Deng
This network shows the impact of papers produced by Yafei Deng. 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 Yafei Deng. The network helps show where Yafei Deng may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Yafei Deng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 15 | |
| 2 | A Calibration-Based Hybrid Transfer Learning Framework for RUL Prediction of Rolling Bearing Across Different Machinesbreakdown → | 2023 | 125 |
| 3 | Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transferbreakdown → | 2023 | 85 |
| 4 | 2021 | 35 | |
| 5 | 2021 | 81 | |
| 6 | 2021 | 112 | |
| 7 | A double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosisbreakdown → | 2021 | 198 |
| 8 | 2020 | 98 | |
| 9 | 2020 | 30 | |
| 10 | 2019 | 22 | |
| 11 | 2019 | 58 | |
| 12 | 2019 | 2 | |
| 13 | 2019 | 67 | |
| 14 | 2018 | 46 |
About Yafei Deng
Yafei Deng is a scholar working on Industrial and Manufacturing Engineering, Control and Systems Engineering and Mechanical Engineering, having authored 14 papers that have together received 974 indexed citations. Recurring topics across this work include Manufacturing Process and Optimization (7 papers), Machine Fault Diagnosis Techniques (6 papers), Advanced Measurement and Metrology Techniques (3 papers), Advanced machining processes and optimization (3 papers), Non-Destructive Testing Techniques (3 papers), Industrial Vision Systems and Defect Detection (3 papers), Fault Detection and Control Systems (2 papers) and Mechanical stress and fatigue analysis (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (221 citations), Control and Systems Engineering (434 citations) and Medical Laboratory Technology (27 citations). Yafei Deng has collaborated with scholars based in China and Italy. Frequent co-authors include Shichang Du, Guilong Li, Delin Huang, Jun Lv, Jun Lv, Chen Zhao, Chenguang Zhao, Yiping Shao, Dong Wang and Chen Zhao. Their work appears in journals such as Neurocomputing, IEEE Transactions on Instrumentation and Measurement 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.