Aijun Hu

2.7k total citations · 3 hit papers
80 papers, 2.0k citations indexed

About

Aijun Hu is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Aijun Hu has authored 80 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Control and Systems Engineering, 38 papers in Mechanical Engineering and 17 papers in Mechanics of Materials. Recurrent topics in Aijun Hu's work include Machine Fault Diagnosis Techniques (56 papers), Gear and Bearing Dynamics Analysis (32 papers) and Fault Detection and Control Systems (14 papers). Aijun Hu is often cited by papers focused on Machine Fault Diagnosis Techniques (56 papers), Gear and Bearing Dynamics Analysis (32 papers) and Fault Detection and Control Systems (14 papers). Aijun Hu collaborates with scholars based in China, United Kingdom and Japan. Aijun Hu's co-authors include Ling Xiang, Xin Yang, Hao Su, Penghe Wang, Yonggang Xu, Jingxu Li, Yue Zhang, Yue Zhang, Xiaoan Yan and Jianing Liu and has published in prestigious journals such as Applied Energy, IEEE Transactions on Power Electronics and Expert Systems with Applications.

In The Last Decade

Aijun Hu

65 papers receiving 2.0k 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.

Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism

2021 277 citations Ling Xiang, Aijun Hu et al. profile →
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions

2022 190 citations Hao Su, Ling Xiang et al. Mechanical Systems and Signal Processing profile →
Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks

2021 167 citations Ling Xiang, Aijun Hu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aijun Hu China	23	1.3k	783	481	361	298	80	2.0k
Xueli An China	25	856 0.6×	433 0.6×	648 1.3×	267 0.7×	385 1.3×	52	1.9k
Yingning Qiu China	20	857 0.6×	361 0.5×	409 0.9×	158 0.4×	129 0.4×	46	1.5k
Jesús María Pinar-Pérez Spain	11	790 0.6×	406 0.5×	468 1.0×	235 0.7×	149 0.5×	23	1.6k
Viktor Slavkovikj Belgium	8	830 0.6×	596 0.8×	105 0.2×	380 1.1×	216 0.7×	10	1.4k
Francesco Cadini Italy	22	484 0.4×	328 0.4×	411 0.9×	360 1.0×	122 0.4×	99	2.0k
Zhu Mao United States	25	582 0.4×	439 0.6×	268 0.6×	497 1.4×	102 0.3×	75	2.2k
W.Y. Liu China	14	753 0.6×	396 0.5×	206 0.4×	207 0.6×	110 0.4×	32	1.1k
Xianguang Kong China	21	775 0.6×	697 0.9×	93 0.2×	271 0.8×	197 0.7×	56	1.4k
Ling Xiang China	32	1.9k 1.4×	1.4k 1.8×	601 1.2×	573 1.6×	368 1.2×	110	2.9k
Xueyi Li China	20	712 0.5×	460 0.6×	196 0.4×	288 0.8×	182 0.6×	50	1.5k

Countries citing papers authored by Aijun Hu

Since Specialization

Citations

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

Fields of papers citing papers by Aijun Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aijun Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Aijun Hu. A scholar is included among the top collaborators of Aijun Hu 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 Aijun Hu. Aijun Hu 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

Xiang, Ling, et al.. (2025). Memory-augmented prototypical meta-learning method for bearing fault identification under few-sample conditions. Neurocomputing. 635. 129996–129996. 3 indexed citations

Hu, Aijun, et al.. (2025). VSMKD: A new deconvolution method and application to rolling bearing fault diagnosis. Applied Acoustics. 240. 110969–110969.

Ling, Xiang, et al.. (2025). A bi-stage adaptive transformer model for condition monitoring of steam turbine generator sets incorporating with shapley additive explanations. Expert Systems with Applications. 299. 130193–130193.

Hu, Aijun, et al.. (2025). Toward a framework of ambiguity: a qualitative understanding of healthcare policy design and governance mechanism in China. Frontiers in Public Health. 13. 1666193–1666193.

Hu, Aijun, et al.. (2025). Vibration and acoustic signal consistent feature fusion network for intelligent bearing fault diagnosis. Engineering Research Express. 7(3). 35206–35206. 1 indexed citations

Hu, Aijun, et al.. (2025). A multi-domain Collaborative bearing data generation model for improving the comprehensive quality of generated samples. Engineering Applications of Artificial Intelligence. 164. 113324–113324.

Xiang, Ling, et al.. (2024). A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions. Expert Systems with Applications. 262. 125686–125686. 12 indexed citations

Zhu, Guopeng, et al.. (2024). A novel stochastic process diffusion model for wind turbines condition monitoring and fault identification with multi-parameter information fusion. Mechanical Systems and Signal Processing. 214. 111397–111397. 22 indexed citations

Su, Hao, et al.. (2024). Semi-Supervised Temporal Meta-Learning Framework for Wind Turbine Bearing Fault Diagnosis Under Limited Annotation Data. IEEE Transactions on Instrumentation and Measurement. 73. 1–9. 11 indexed citations

10.

Su, Hao, Ling Xiang, & Aijun Hu. (2024). Application of deep learning to fault diagnosis of rotating machineries. Measurement Science and Technology. 35(4). 42003–42003. 34 indexed citations

11.

Sun, Yuyan, et al.. (2023). A Prognosis Swin Transformer Network for Rolling Bearing Remaining Useful Life Prediction. 61. 1–6.

12.

Hu, Aijun, et al.. (2023). A novel vision transformer network for rolling bearing remaining useful life prediction. Measurement Science and Technology. 35(2). 25106–25106. 6 indexed citations

13.

Ma, Chaoyong, et al.. (2023). Periodic Detection Mode Decomposition and Its Application in Bearing Fault Diagnosis. IEEE Sensors Journal. 23(11). 11806–11814. 3 indexed citations

14.

Hu, Aijun, et al.. (2022). Rotating machinery fault diagnosis based on impact feature extraction deep neural network. Measurement Science and Technology. 33(11). 114004–114004. 22 indexed citations

15.

Xiang, Ling, et al.. (2022). A new condition-monitoring method based on multi-variable correlation learning network for wind turbine fault detection. Measurement Science and Technology. 34(2). 24009–24009. 13 indexed citations

16.

Xiang, Ling, et al.. (2022). Impact of Wind Power Penetration on Wind–Thermal-Bundled Transmission System. IEEE Transactions on Power Electronics. 37(12). 15616–15625. 22 indexed citations

17.

Zhang, Kun, Haihong Tang, Peng Chen, Yonggang Xu, & Aijun Hu. (2021). A Method for Extracting Fault Features Using Variable Multilevel Spectral Segmentation Framework and Harmonic Correlation Index. IEEE Transactions on Instrumentation and Measurement. 71. 1–9. 8 indexed citations

18.

Hu, Aijun, et al.. (2019). Frequency Loss and Recovery in Rolling Bearing Fault Detection. Chinese Journal of Mechanical Engineering. 32(1). 5 indexed citations

19.

Hu, Aijun, et al.. (2019). An engineering condition indicator for condition monitoring of wind turbine bearings. Wind Energy. 23(2). 207–219. 21 indexed citations

20.

Hu, Aijun, et al.. (2017). A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing. Shock and Vibration. 2017. 1–11. 7 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.

Explore authors with similar magnitude of impact