Zenghui An

1.9k total citations · 1 hit paper
43 papers, 1.6k citations indexed

About

Zenghui An is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Zenghui An has authored 43 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Control and Systems Engineering, 27 papers in Mechanical Engineering and 23 papers in Mechanics of Materials. Recurrent topics in Zenghui An's work include Machine Fault Diagnosis Techniques (36 papers), Engineering Diagnostics and Reliability (22 papers) and Gear and Bearing Dynamics Analysis (21 papers). Zenghui An is often cited by papers focused on Machine Fault Diagnosis Techniques (36 papers), Engineering Diagnostics and Reliability (22 papers) and Gear and Bearing Dynamics Analysis (21 papers). Zenghui An collaborates with scholars based in China and Germany. Zenghui An's co-authors include Shunming Li, Jinrui Wang, Xingxing Jiang, Yu Xin, Weiwei Qian, Kun Xu, Shanshan Ji, Baokun Han, Huaihai Chen and Zongzhen Zhang and has published in prestigious journals such as IEEE Access, IEEE Transactions on Industrial Informatics and Mechanical Systems and Signal Processing.

In The Last Decade

Zenghui An

42 papers receiving 1.5k citations

Hit Papers

Batch-normalized deep neural networks for achieving fast ... 2018 2026 2020 2023 2018 50 100 150 200
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.

  1. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines
    2018 219 citations Jinrui Wang, Shunming Li et al. Neurocomputing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zenghui An China 22 1.4k 844 474 250 138 43 1.6k
Dandan Peng China 16 1.4k 1.0× 829 1.0× 429 0.9× 267 1.1× 202 1.5× 39 1.8k
Grover Zurita China 14 1.4k 1.0× 921 1.1× 519 1.1× 195 0.8× 136 1.0× 25 1.8k
Yiming Xiao China 13 1.1k 0.8× 577 0.7× 339 0.7× 333 1.3× 114 0.8× 23 1.4k
Wenguang Yang China 11 994 0.7× 639 0.8× 338 0.7× 254 1.0× 95 0.7× 22 1.3k
Xiaoxi Ding China 22 1.3k 1.0× 963 1.1× 517 1.1× 172 0.7× 200 1.4× 100 1.8k
Shen Yan China 12 1.2k 0.8× 532 0.6× 360 0.8× 369 1.5× 130 0.9× 21 1.5k
Yixiao Liao China 13 1.5k 1.1× 854 1.0× 458 1.0× 419 1.7× 107 0.8× 19 1.9k
Kaixuan Liang China 17 1.1k 0.8× 664 0.8× 338 0.7× 196 0.8× 87 0.6× 30 1.3k
Yuantao Yang China 12 1.1k 0.8× 623 0.7× 371 0.8× 181 0.7× 91 0.7× 17 1.3k

Countries citing papers authored by Zenghui An

Since Specialization
Citations

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

Fields of papers citing papers by Zenghui An

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zenghui An

This figure shows the co-authorship network connecting the top 25 collaborators of Zenghui An. A scholar is included among the top collaborators of Zenghui An 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 Zenghui An. Zenghui An 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.
Weigel, Scott J., et al.. (2025). Surrogate Model-Based Drive Cycle Modeling and Optimization of Synchronous Reluctance Machines for Electric Vehicles. IEEE Transactions on Magnetics. 61(9). 1–6. 1 indexed citations
2.
An, Zenghui, et al.. (2024). Contrast learning with hard example mining for few-shot fault diagnosis of rolling bearings. Measurement Science and Technology. 35(10). 106121–106121. 2 indexed citations
3.
An, Zenghui, et al.. (2024). A dual-weight mechanism-based neural network for partial domain adaptation fault diagnosis of bearings under different working conditions. Measurement Science and Technology. 36(1). 16173–16173. 1 indexed citations
4.
Yang, Rui, et al.. (2024). Dilated dynamic supervised contrastive learning framework for fault diagnosis under imbalanced dataset conditions. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. 239(7). 2626–2636. 1 indexed citations
5.
6.
An, Zenghui, et al.. (2023). Few-shot Fault Diagnosis Based on Supervised Contrast Learning. 46. 1–6. 1 indexed citations
7.
8.
9.
Li, Shunming, Zenghui An, & Jiantao Lu. (2020). A Novel Data-Driven Fault Feature Separation Method and Its Application on Intelligent Fault Diagnosis Under Variable Working Conditions. IEEE Access. 8. 113702–113712. 23 indexed citations
10.
Xin, Yu, Shunming Li, Jinrui Wang, Zenghui An, & Zhang We. (2020). Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi‐object deep CNN. IET Science Measurement & Technology. 14(4). 407–415. 19 indexed citations
11.
Chen, Huaihai, et al.. (2020). A novel unsupervised domain adaptation based on deep neural network and manifold regularization for mechanical fault diagnosis. Measurement Science and Technology. 31(8). 85101–85101. 8 indexed citations
12.
Chen, Huaihai, et al.. (2020). Sparse filtering based domain adaptation for mechanical fault diagnosis. Neurocomputing. 393. 101–111. 28 indexed citations
13.
An, Zenghui, et al.. (2019). An intelligent fault diagnosis framework dealing with arbitrary length inputs under different working conditions. Measurement Science and Technology. 30(12). 125107–125107. 26 indexed citations
14.
Xu, Kun, et al.. (2019). A novel convolutional transfer feature discrimination network for unbalanced fault diagnosis under variable rotational speeds. Measurement Science and Technology. 30(10). 105107–105107. 31 indexed citations
15.
Xu, Kun, et al.. (2019). A renewable fusion fault diagnosis network for the variable speed conditions under unbalanced samples. Neurocomputing. 379. 12–29. 43 indexed citations
16.
An, Zenghui, Shunming Li, Jinrui Wang, & Xingxing Jiang. (2019). A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network. ISA Transactions. 100. 155–170. 183 indexed citations
17.
Zhang, Zongzhen, Shunming Li, Jinrui Wang, Yu Xin, & Zenghui An. (2019). General normalized sparse filtering: A novel unsupervised learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing. 124. 596–612. 102 indexed citations
18.
Xu, Kun, Shunming Li, Jinrui Wang, Zenghui An, & Yu Xin. (2019). A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering. 234(4). 1167–1182. 14 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.
Li, Shunming, et al.. (2017). Study on Finite Element Method of Stress Field in Aluminum Alloy High-Speed Milling Process. IOP Conference Series Materials Science and Engineering. 269. 12088–12088. 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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