Baokun Han

2.4k total citations · 1 hit paper
107 papers, 1.8k citations indexed

About

Baokun Han is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Baokun Han has authored 107 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Control and Systems Engineering, 60 papers in Mechanical Engineering and 27 papers in Mechanics of Materials. Recurrent topics in Baokun Han's work include Machine Fault Diagnosis Techniques (74 papers), Gear and Bearing Dynamics Analysis (49 papers) and Fault Detection and Control Systems (23 papers). Baokun Han is often cited by papers focused on Machine Fault Diagnosis Techniques (74 papers), Gear and Bearing Dynamics Analysis (49 papers) and Fault Detection and Control Systems (23 papers). Baokun Han collaborates with scholars based in China, United States and Australia. Baokun Han's co-authors include Jinrui Wang, Huaiqian Bao, Zongzhen Zhang, Shanshan Ji, Guifang Liu, Fanqianhui Yu, Xingxing Jiang, Tao Lü, Shunming Li and Sixiang Jia and has published in prestigious journals such as Scientific Reports, Chemical Engineering Journal and IEEE Access.

In The Last Decade

Baokun Han

101 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis

2023 110 citations Jinrui Wang, Zongzhen Zhang et al. Reliability Engineering & System Safety profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Baokun Han China	23	1.2k	771	348	286	199	107	1.8k
Govind Vashishtha India	26	976 0.8×	772 1.0×	305 0.9×	324 1.1×	259 1.3×	83	1.7k
Zhenghong Wu China	24	1.2k 1.1×	847 1.1×	377 1.1×	352 1.2×	145 0.7×	45	1.8k
Yiwei Cheng China	16	1.3k 1.1×	774 1.0×	418 1.2×	196 0.7×	197 1.0×	46	1.8k
Sheng Xiang China	18	965 0.8×	640 0.8×	333 1.0×	167 0.6×	137 0.7×	40	1.6k
Dandan Peng China	16	1.4k 1.2×	829 1.1×	429 1.2×	267 0.9×	202 1.0×	39	1.8k
Zitong Zhou China	29	1.8k 1.5×	1.1k 1.4×	505 1.5×	501 1.8×	345 1.7×	74	2.4k
Xu Li China	19	1.3k 1.1×	1.4k 1.8×	593 1.7×	348 1.2×	309 1.6×	93	2.6k
Xueyi Li China	20	712 0.6×	460 0.6×	288 0.8×	182 0.6×	196 1.0×	50	1.5k
Peng Ding China	24	1.2k 1.0×	696 0.9×	304 0.9×	407 1.4×	136 0.7×	103	2.1k
Enyong Xu China	16	762 0.7×	446 0.6×	226 0.6×	270 0.9×	197 1.0×	57	1.2k

Countries citing papers authored by Baokun Han

Since Specialization

Citations

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

Fields of papers citing papers by Baokun Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baokun Han

This figure shows the co-authorship network connecting the top 25 collaborators of Baokun Han. A scholar is included among the top collaborators of Baokun 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 Baokun Han. Baokun Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Jinrui, Dawei Wang, Huaiqian Bao, et al.. (2025). A new adaptive multi-scale attention adversarial network for cross-domain fault diagnosis. Knowledge-Based Systems. 311. 113066–113066. 3 indexed citations

Wang, Jinrui, et al.. (2024). Self-learning guided residual shrinkage network for intelligent fault diagnosis of planetary gearbox. Engineering Applications of Artificial Intelligence. 139. 109603–109603. 1 indexed citations

Han, Baokun, et al.. (2024). Adaptive fast nonlinear blind deconvolution based on nonuniform particle swarm optimization for the rolling bearing fault diagnosis. Measurement. 235. 114788–114788. 6 indexed citations

Wang, Jinrui, et al.. (2024). A New Dual-Domain Signal Collaborative Transfer Network for Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 73. 1–11. 9 indexed citations

Bao, Huaiqian, et al.. (2024). A novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network. Measurement Science and Technology. 35(10). 106117–106117. 3 indexed citations

Han, Baokun, Shujun Wang, Feng Tang, et al.. (2024). A quenching electrochemiluminescence energy resonance transfer system based on CdS and COFs for the ultrasensitive detection of CA242. New Journal of Chemistry. 48(28). 12733–12739.

Wang, Jinrui, Zongzhen Zhang, Zhiliang Liu, et al.. (2023). Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis. Reliability Engineering & System Safety. 234. 109152–109152. 110 indexed citations breakdown →

Zhang, Zongzhen, et al.. (2023). A novel bearing diagnosis method under random impact disturbance based on the nonlinear convolutional sparse filtering. Measurement Science and Technology. 34(10). 105103–105103. 2 indexed citations

Han, Rui, et al.. (2023). Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition. Measurement Science and Technology. 35(4). 42001–42001. 8 indexed citations

10.

Han, Baokun, et al.. (2023). A Novel Domain Adaptive Fault Diagnosis Method for Bearings Based on Unbalance Data Generation. IEEE Transactions on Instrumentation and Measurement. 72. 1–11. 15 indexed citations

11.

Han, Baokun, et al.. (2023). An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions. Transactions of the Institute of Measurement and Control. 46(5). 927–937. 7 indexed citations

12.

Han, Baokun, et al.. (2023). A new multichannel deep adaptive adversarial network for cross-domain fault diagnosis. Measurement Science and Technology. 34(6). 65002–65002. 10 indexed citations

13.

Wang, Xiaojing, et al.. (2023). A novel hybrid distance guided domain adversarial method for cross domain fault diagnosis of gearbox. Measurement Science and Technology. 34(6). 65115–65115. 13 indexed citations

14.

Zhang, Xiao, Jinrui Wang, Sixiang Jia, Baokun Han, & Zongzhen Zhang. (2022). Partial Domain Adaptation Method Based on Class-Weighted Alignment for Fault Diagnosis of Rotating Machinery. IEEE Transactions on Instrumentation and Measurement. 71. 1–14. 40 indexed citations

15.

Wang, Jinrui, et al.. (2022). Fault diagnosis of rotating machinery in a noisy environment based on modified general normalized sparse filtering. Measurement Science and Technology. 33(11). 115107–115107. 2 indexed citations

16.

Zhang, Zongzhen, et al.. (2022). Fast nonlinear Hoyergram for bearings fault diagnosis under random impact interference. Measurement Science and Technology. 33(7). 75112–75112. 20 indexed citations

17.

Zhang, Xiao, et al.. (2021). A novel transfer-learning method based on selective normalization for fault diagnosis with limited labeled data. Measurement Science and Technology. 32(10). 105116–105116. 13 indexed citations

18.

Jia, Meixia, et al.. (2021). A novel method for diagnosing bearing transfer faults based on a maximum mean discrepancies guided domain-adversarial mechanism. Measurement Science and Technology. 33(1). 15109–15109. 33 indexed citations

19.

Wang, Jinrui, Shunming Li, Baokun Han, et al.. (2018). Construction of a batch-normalized autoencoder network and its application in mechanical intelligent fault diagnosis. Measurement Science and Technology. 30(1). 15106–15106. 57 indexed citations

20.

Han, Baokun, et al.. (2016). Ultra-short Baseline Localization Using Five-Element Arrays. 38(5). 57. 1 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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