Jingyan Xia
- Control and Systems Engineering top 5%
- Mechanical Engineering top 10%
- Mechanics of Materials top 10%
- Artificial Intelligence
- Industrial and Manufacturing Engineering top 5%
- Topics
- Machine Fault Diagnosis Techniques (12 papers)Engineering Diagnostics and Reliability (5 papers)Advanced machining processes and optimization (4 papers)
- Cited by
- Control and Systems EngineeringIndustrial and Manufacturing EngineeringMechanical Engineering
In The Last Decade
Jingyan Xia
11 papers receiving 400 citations
Hit Papers
Peers
Comparison fields: 5 of 36
- Control and Systems Engineering 297
- Mechanical Engineering 175
- Mechanics of Materials 106
- Artificial Intelligence 75
- Industrial and Manufacturing Engineering 64
Countries citing papers authored by Jingyan Xia
This map shows the geographic impact of Jingyan Xia'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 Jingyan Xia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingyan Xia more than expected).
Fields of papers citing papers by Jingyan Xia
This network shows the impact of papers produced by Jingyan Xia. 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 Jingyan Xia. The network helps show where Jingyan Xia may publish in the future.
Co-authorship network of co-authors of Jingyan Xia
This figure shows the co-authorship network connecting the top 25 collaborators of Jingyan Xia. A scholar is included among the top collaborators of Jingyan Xia 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 Jingyan Xia. Jingyan Xia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 32 | |
| 2 | 40 | |
| 3 | 10 | |
| 4 | Knowledge Embedded Autoencoder Network for Harmonic Drive Fault Diagnosis Under Few-Shot Industrial Scenariosbreakdown → | 58 |
| 5 | Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning networkbreakdown → | 88 |
| 6 | 57 | |
| 7 | 79 | |
| 8 | 1 | |
| 9 | 25 | |
| 10 | 4 | |
| 11 | 13 | |
| 12 | 0 | |
| 13 | 3 |
About Jingyan Xia
Jingyan Xia is a scholar working on Control and Systems Engineering, Mechanics of Materials and Mechanical Engineering, having authored 13 papers that have together received 410 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (12 papers), Engineering Diagnostics and Reliability (5 papers) and Advanced machining processes and optimization (4 papers). The work is most often cited by research in Control and Systems Engineering (297 citations), Industrial and Manufacturing Engineering (64 citations) and Mechanical Engineering (175 citations). Jingyan Xia has collaborated with scholars based in China and Hong Kong. Frequent co-authors include Weihua Li, Zhuyun Chen, Ruyi Huang, Jipu Li, Guolin He, Jiaxian Chen, Junbin Chen, Gang Jin, Bin Zhang and Yixiao Liao. Their work appears in journals such as IEEE Internet of Things Journal, Reliability Engineering & System Safety and IEEE Transactions on Instrumentation 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.