Shengnan Tang

4.0k total citations · 6 hit papers
89 papers, 3.2k citations indexed

About

Shengnan Tang is a scholar working on Control and Systems Engineering, Mechanical Engineering and Materials Chemistry. According to data from OpenAlex, Shengnan Tang has authored 89 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Control and Systems Engineering, 25 papers in Mechanical Engineering and 23 papers in Materials Chemistry. Recurrent topics in Shengnan Tang's work include Machine Fault Diagnosis Techniques (26 papers), Hydraulic and Pneumatic Systems (22 papers) and Oil and Gas Production Techniques (19 papers). Shengnan Tang is often cited by papers focused on Machine Fault Diagnosis Techniques (26 papers), Hydraulic and Pneumatic Systems (22 papers) and Oil and Gas Production Techniques (19 papers). Shengnan Tang collaborates with scholars based in China, Singapore and Malaysia. Shengnan Tang's co-authors include Yong Zhu, Shouqi Yuan, Shifa Wang, Huajing Gao, Guangpeng Li, Hong Su, Leiming Fang, Zao Yi, Xinxin Zhao and Chuan Yu and has published in prestigious journals such as Chemical Communications, ACS Catalysis and International Journal of Hydrogen Energy.

In The Last Decade

Shengnan Tang

84 papers receiving 3.2k citations

Hit Papers

Deep Learning-Based Intelligent Fault Diagnosis Methods T... 2019 2026 2021 2023 2019 2021 2022 2022 2022 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. Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
    2019 230 citations Shengnan Tang, Shouqi Yuan et al. IEEE Access profile →
  2. An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump
    2021 158 citations Shengnan Tang, Yong Zhu et al. profile →
  3. Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization
    2022 132 citations Shengnan Tang, Yong Zhu et al. ISA Transactions profile →
  4. A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images
    2022 127 citations Shengnan Tang, Yong Zhu et al. profile →
  5. Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform
    2022 120 citations Shengnan Tang, Yong Zhu et al. profile →
  6. Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery
    2024 65 citations Shengnan Tang, Yong Zhu et al. Engineering Applications of Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shengnan Tang China 33 1.2k 917 724 621 497 89 3.2k
Jinrui Wang China 44 2.3k 2.0× 1.4k 1.6× 948 1.3× 487 0.8× 833 1.7× 256 5.9k
Yudong Cao China 26 1.1k 0.9× 667 0.7× 278 0.4× 321 0.5× 334 0.7× 68 2.7k
Xuan Zhao China 29 396 0.3× 1.5k 1.6× 319 0.4× 151 0.2× 391 0.8× 277 3.8k
Cheng Chen China 24 300 0.3× 728 0.8× 497 0.7× 91 0.1× 146 0.3× 147 2.4k
Changming Cheng China 20 723 0.6× 487 0.5× 190 0.3× 164 0.3× 227 0.5× 68 1.8k
Ying Chen China 33 352 0.3× 1.2k 1.3× 645 0.9× 1.2k 1.9× 106 0.2× 176 4.2k
Shibo Wang China 35 158 0.1× 550 0.6× 1.1k 1.5× 162 0.3× 492 1.0× 247 5.4k
V.S. Muralidharan India 21 693 0.6× 617 0.7× 427 0.6× 101 0.2× 282 0.6× 100 1.9k
Sang Bin Lee South Korea 48 4.1k 3.5× 2.5k 2.7× 671 0.9× 129 0.2× 689 1.4× 294 7.1k
Chun Wang China 33 119 0.1× 988 1.1× 1.3k 1.8× 256 0.4× 779 1.6× 181 3.5k

Countries citing papers authored by Shengnan Tang

Since Specialization
Citations

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

Fields of papers citing papers by Shengnan Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shengnan Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Shengnan Tang. A scholar is included among the top collaborators of Shengnan Tang 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 Shengnan Tang. Shengnan Tang 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.
2.
Zhu, Yong, et al.. (2025). Research Progress and Applications of Artificial Intelligence in Agricultural Equipment. Agriculture. 15(15). 1703–1703. 2 indexed citations
3.
Tang, Shengnan, et al.. (2025). Bayesian algorithm optimized deep model for multi-signal fault identification of hydraulic pumps. Alexandria Engineering Journal. 121. 465–483. 2 indexed citations
4.
Tang, Shengnan, et al.. (2025). Deep transferable model with multi-head attention for small sample fault diagnosis of hydraulic pumps. Engineering Applications of Artificial Intelligence. 160. 111808–111808.
5.
Zhu, Yong, et al.. (2024). Dynamic Characteristics Analysis of the DI-SO Cylindrical Spur Gear System Based on Meshing Conditions. Journal of Marine Science and Engineering. 12(9). 1589–1589.
6.
Zhu, Yong, et al.. (2023). A Novel Fault Diagnosis Method Based on SWT and VGG-LSTM Model for Hydraulic Axial Piston Pump. Journal of Marine Science and Engineering. 11(3). 594–594. 26 indexed citations
7.
Zhu, Yong, et al.. (2023). Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review. Journal of Marine Science and Engineering. 11(8). 1609–1609. 15 indexed citations
8.
Zhu, Yong, Tao Zhou, Shengnan Tang, & Shouqi Yuan. (2023). A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump. Journal of Marine Science and Engineering. 11(7). 1273–1273. 9 indexed citations
9.
Zhu, Yong, Tao Zhou, Shengnan Tang, & Shouqi Yuan. (2023). Failure Analysis and Intelligent Identification of Critical Friction Pairs of an Axial Piston Pump. Journal of Marine Science and Engineering. 11(3). 616–616. 6 indexed citations
10.
Tang, Shengnan, Yong Zhu, & Shouqi Yuan. (2022). Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization. ISA Transactions. 129(Pt A). 555–563. 132 indexed citations breakdown →
11.
Du, Juan, et al.. (2022). Hydrogen sulphide treatment improves the inhibition byvps4 gene knockdown in silkworm (Bombyx mori). Journal of Insects as Food and Feed. 9(1). 101–108. 3 indexed citations
12.
Zhu, Yong, et al.. (2021). Instability Condition Derivation for Hydraulic AGC System under Pressure Closed‐Loop Control. Shock and Vibration. 2021(1). 1 indexed citations
13.
Zhu, Yong, Guangpeng Li, Rui Wang, et al.. (2021). Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet. Sensors. 21(2). 549–549. 43 indexed citations
14.
Liu, Xinyi, et al.. (2020). Fabrication and Photoluminescence Properties of MgAl2O4: Mg Phosphors. Cailiao yanjiu xuebao. 34(10). 784–792. 3 indexed citations
15.
Tang, Shengnan, Yong Zhu, Shouqi Yuan, & Guangpeng Li. (2020). Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model. Sensors. 20(24). 7152–7152. 19 indexed citations
16.
Tang, Shengnan, Shouqi Yuan, Yong Zhu, & Guangpeng Li. (2020). An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump. Sensors. 20(22). 6576–6576. 30 indexed citations
17.
Tang, Shengnan, Shouqi Yuan, & Yong Zhu. (2020). Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery. Processes. 8(10). 1217–1217. 6 indexed citations
18.
Tang, Shengnan, Shouqi Yuan, & Yong Zhu. (2020). Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery. IEEE Access. 8. 86510–86519. 117 indexed citations
19.
Zhu, Yong, et al.. (2019). Bifurcation Characteristic Research on the Load Vertical Vibration of a Hydraulic Automatic Gauge Control System. Processes. 7(10). 718–718. 28 indexed citations
20.
Tang, Shengnan. (2010). Research on Transformer Fault Diagnosis based on Neural Network. Microcomputer Information.

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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