Xiaohang Jin

2.5k total citations · 2 hit papers
54 papers, 2.1k citations indexed

About

Xiaohang Jin is a scholar working on Control and Systems Engineering, Mechanical Engineering and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Xiaohang Jin has authored 54 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Control and Systems Engineering, 18 papers in Mechanical Engineering and 14 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Xiaohang Jin's work include Machine Fault Diagnosis Techniques (33 papers), Fault Detection and Control Systems (18 papers) and Gear and Bearing Dynamics Analysis (11 papers). Xiaohang Jin is often cited by papers focused on Machine Fault Diagnosis Techniques (33 papers), Fault Detection and Control Systems (18 papers) and Gear and Bearing Dynamics Analysis (11 papers). Xiaohang Jin collaborates with scholars based in China, Hong Kong and United States. Xiaohang Jin's co-authors include Tommy W. S. Chow, Mingbo Zhao, Michael Pecht, Yu Wang, Zijun Que, Zhengguo Xu, Wei Qiao, Yi Sun, Kwok‐Leung Tsui and Yizhen Peng and has published in prestigious journals such as Journal of Cleaner Production, IEEE Transactions on Industrial Electronics and Expert Systems with Applications.

In The Last Decade

Xiaohang Jin

54 papers receiving 2.0k citations

Hit Papers

Motor Bearing Fault Diagnosis Using Trace Ratio Linear Di... 2013 2026 2017 2021 2013 2016 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Xiaohang Jin China 20 1.6k 853 365 364 260 54 2.1k
Yiwei Cheng China 16 1.3k 0.8× 774 0.9× 418 1.1× 279 0.8× 197 0.8× 46 1.8k
Lotfi Saïdi Tunisia 18 1.9k 1.2× 1.2k 1.5× 600 1.6× 324 0.9× 282 1.1× 58 2.4k
Brigitte Chebel‐Morello France 14 1.3k 0.8× 757 0.9× 401 1.1× 308 0.8× 127 0.5× 33 1.7k
Cheng‐Geng Huang China 21 986 0.6× 540 0.6× 228 0.6× 316 0.9× 334 1.3× 37 1.7k
Wennian Yu China 23 1.0k 0.7× 1.4k 1.6× 536 1.5× 289 0.8× 148 0.6× 84 2.2k
Tian Ran Lin China 22 1.2k 0.8× 766 0.9× 477 1.3× 242 0.7× 128 0.5× 79 1.9k
Sheng Xiang China 18 965 0.6× 640 0.8× 333 0.9× 234 0.6× 137 0.5× 40 1.6k
Weining Lu China 15 1.3k 0.8× 721 0.8× 355 1.0× 148 0.4× 133 0.5× 33 1.8k
Jian Ma China 23 1.6k 1.1× 924 1.1× 589 1.6× 285 0.8× 811 3.1× 113 2.9k
Hongfu Zuo China 20 747 0.5× 495 0.6× 297 0.8× 314 0.9× 321 1.2× 163 1.7k

Countries citing papers authored by Xiaohang Jin

Since Specialization
Citations

This map shows the geographic impact of Xiaohang Jin'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 Xiaohang Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaohang Jin more than expected).

Fields of papers citing papers by Xiaohang Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaohang Jin. 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 Xiaohang Jin. The network helps show where Xiaohang Jin may publish in the future.

Co-authorship network of co-authors of Xiaohang Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaohang Jin. A scholar is included among the top collaborators of Xiaohang Jin 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 Xiaohang Jin. Xiaohang Jin 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.
Kong, Ziqian, et al.. (2024). Spatio-Temporal Propagation: An Extended Message-Passing Graph Neural Network for Remaining Useful Life Prediction. IEEE Sensors Journal. 24(20). 32468–32479. 4 indexed citations
2.
Jin, Xiaohang, et al.. (2024). Graph Spatio-Temporal Networks for Condition Monitoring of Wind Turbine. IEEE Transactions on Sustainable Energy. 15(4). 2276–2286. 6 indexed citations
3.
Liu, Zhen, et al.. (2024). Improved Autoencoder Model With Memory Module for Anomaly Detection. IEEE Sensors Journal. 24(8). 12770–12781. 4 indexed citations
4.
Jiang, Guoqian, et al.. (2024). Spatio-Temporal Feature Alignment Transfer Learning for Cross-Turbine Blade Icing Detection of Wind Turbines. IEEE Transactions on Instrumentation and Measurement. 73. 1–17. 15 indexed citations
5.
Wang, Ziqi, Xiaohang Jin, & Zhengguo Xu. (2024). Incremental Learning Method for Wind Turbine Fault Detection Models Considering False Negatives. IEEE Transactions on Instrumentation and Measurement. 73. 1–13. 2 indexed citations
6.
Jiang, Guoqian, et al.. (2024). Current-Aided Vibration Fusion Network for Fault Diagnosis in Electromechanical Drive System. IEEE Transactions on Instrumentation and Measurement. 73. 1–10. 11 indexed citations
8.
Kong, Ziqian, et al.. (2024). Remaining useful life prediction with uncertainty quantification based on multi-distribution fusion structure. Reliability Engineering & System Safety. 251. 110383–110383. 12 indexed citations
9.
Xiao, Gang, et al.. (2024). Knowledge Graph Metric Learning Network for Few-Shot Health Status Assessment. IEEE Sensors Journal. 25(2). 3898–3908. 2 indexed citations
10.
Jin, Xiaohang, et al.. (2023). Condition Monitoring of Wind Turbine Generators Based on SCADA Data and Feature Transfer Learning. IEEE Access. 11. 9441–9450. 6 indexed citations
11.
Wang, Ziqi, et al.. (2023). A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems. Complex & Intelligent Systems. 10(1). 655–675. 6 indexed citations
12.
Zhang, Yuanming, et al.. (2023). Temporal Knowledge Graph Informer Network for Remaining Useful Life Prediction. IEEE Transactions on Instrumentation and Measurement. 72. 1–10. 11 indexed citations
13.
Jin, Xiaohang, et al.. (2023). A Physics-Based and Data-Driven Feature Extraction Model for Blades Icing Detection of Wind Turbines. IEEE Sensors Journal. 23(4). 3944–3954. 19 indexed citations
14.
Jin, Xiaohang, et al.. (2022). Condition Monitoring of Wind Turbine Generator Based on Transfer Learning and One-Class Classifier. IEEE Sensors Journal. 22(24). 24130–24139. 19 indexed citations
15.
Que, Zijun, Xiaohang Jin, & Zhengguo Xu. (2021). Remaining Useful Life Prediction for Bearings Based on a Gated Recurrent Unit. IEEE Transactions on Instrumentation and Measurement. 70. 1–11. 106 indexed citations
16.
Jin, Xiaohang, et al.. (2020). Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis. IEEE Transactions on Sustainable Energy. 12(1). 202–210. 146 indexed citations
17.
Jin, Xiaohang, et al.. (2019). A Data-Driven Approach for Bearing Fault Prognostics. IEEE Transactions on Industry Applications. 55(4). 3394–3401. 67 indexed citations
18.
Jin, Xiaohang, Fangzhou Cheng, Yayu Peng, Wei Qiao, & Liyan Qu. (2018). Drivetrain Gearbox Fault Diagnosis: Vibration- and Current-Based Approaches. IEEE Industry Applications Magazine. 24(6). 56–66. 25 indexed citations
19.
Sun, Yi, et al.. (2017). Experimental and Modeling Study of the Regular Polygon Angle-spiral Liner in Ball Mills. Chinese Journal of Mechanical Engineering. 30(2). 363–372. 2 indexed citations
20.
Jin, Xiaohang, Yi Sun, Zijun Que, Yu Wang, & Tommy W. S. Chow. (2016). Anomaly Detection and Fault Prognosis for Bearings. IEEE Transactions on Instrumentation and Measurement. 65(9). 2046–2054. 189 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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