Daichao Wang

573 total citations · 1 hit paper
17 papers, 429 citations indexed

About

Daichao Wang is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Daichao Wang has authored 17 papers receiving a total of 429 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Control and Systems Engineering, 6 papers in Mechanical Engineering and 5 papers in Mechanics of Materials. Recurrent topics in Daichao Wang's work include Machine Fault Diagnosis Techniques (12 papers), Fault Detection and Control Systems (8 papers) and Gear and Bearing Dynamics Analysis (6 papers). Daichao Wang is often cited by papers focused on Machine Fault Diagnosis Techniques (12 papers), Fault Detection and Control Systems (8 papers) and Gear and Bearing Dynamics Analysis (6 papers). Daichao Wang collaborates with scholars based in China and United Kingdom. Daichao Wang's co-authors include Yibin Li, Yan Song, Shengyao Gao, Lei Jia, Yanjun Liu, Tao Wen, Hui Li, Yan Song, Jian Xing and Yongwei Tang and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Industrial Informatics and IEEE Transactions on Instrumentation and Measurement.

In The Last Decade

Daichao Wang

16 papers receiving 419 citations

Hit Papers

Intelligent Fault Diagnosis Method Based on Full 1-D Conv... 2019 2026 2021 2023 2019 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. Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network
    2019 205 citations Yibin Li, Daichao Wang et al. IEEE Transactions on Industrial Informatics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daichao Wang China 7 351 193 103 67 57 17 429
Dongfang Zhao China 12 347 1.0× 205 1.1× 120 1.2× 80 1.2× 47 0.8× 28 471
Diwang Ruan China 9 356 1.0× 215 1.1× 104 1.0× 64 1.0× 30 0.5× 29 450
Guoqiang Li China 8 367 1.0× 207 1.1× 137 1.3× 55 0.8× 37 0.6× 17 438
Jianping Yan China 9 335 1.0× 198 1.0× 103 1.0× 64 1.0× 29 0.5× 23 423
Biliang Lu China 11 368 1.0× 194 1.0× 103 1.0× 104 1.6× 35 0.6× 18 473
Zhen Shi China 10 305 0.9× 170 0.9× 103 1.0× 68 1.0× 32 0.6× 24 401
Jingyan Xia China 9 297 0.8× 175 0.9× 106 1.0× 75 1.1× 30 0.5× 13 410
Jorge Nei Brito Brazil 6 278 0.8× 139 0.7× 90 0.9× 89 1.3× 57 1.0× 15 387
Zhiwu Shang China 12 319 0.9× 170 0.9× 102 1.0× 94 1.4× 31 0.5× 40 411

Countries citing papers authored by Daichao Wang

Since Specialization
Citations

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

Fields of papers citing papers by Daichao Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daichao Wang

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

All Works

17 of 17 papers shown
1.
Li, Yibin, et al.. (2025). A novel reward-shaping-based soft actor–critic for random trajectory tracking of AUVs. Ocean Engineering. 322. 120505–120505. 1 indexed citations
2.
Wang, Daichao, et al.. (2025). Bearing fault diagnosis method based on multi-level information fusion. Advanced Engineering Informatics. 66. 103405–103405. 5 indexed citations
3.
Wang, Daichao, et al.. (2025). Bearing Fault Diagnosis Method Based on Multisensor Hybrid Feature Fusion. IEEE Transactions on Instrumentation and Measurement. 74. 1–11. 1 indexed citations
4.
Song, Yan, et al.. (2025). Fault Diagnosis in Rolling Bearings Using Multi-Gaussian Attention and Covariance Loss for Single Domain Generalization. IEEE Transactions on Instrumentation and Measurement. 74. 1–10. 1 indexed citations
5.
Li, Yibin, et al.. (2025). Multi-scale Gaussian feature enhancement and prototype graph convolutional network for domain-generalized rolling bearing fault diagnosis. Expert Systems with Applications. 296. 129218–129218. 1 indexed citations
6.
Han, Yang, et al.. (2025). A global information-guided denoising diffusion probabilistic model for fault diagnosis with imbalanced data. Engineering Applications of Artificial Intelligence. 147. 110312–110312. 5 indexed citations
7.
Li, Yibin, et al.. (2024). IEVAEGAN: An Input Enhancement VAEGAN for Rotating Component Fault Diagnosis With Extremely Limited Data. IEEE Sensors Journal. 24(23). 39958–39966. 2 indexed citations
8.
Wang, Daichao, et al.. (2024). Bearing Fault Diagnosis Method Based on Multiple-Level Feature Tensor Fusion. IEEE Sensors Journal. 24(14). 23108–23116. 4 indexed citations
9.
Wang, Daichao, et al.. (2024). A multi-scale feature extraction and fusion method for bearing fault diagnosis based on hybrid attention mechanism. Signal Image and Video Processing. 18(S1). 31–41. 7 indexed citations
10.
Li, Hui & Daichao Wang. (2023). Multilevel feature fusion of multi-domain vibration signals for bearing fault diagnosis. Signal Image and Video Processing. 18(1). 99–108. 7 indexed citations
11.
Wang, Daichao, Yibin Li, Lei Jia, Yan Song, & Tao Wen. (2022). Attention-Based Bilinear Feature Fusion Method for Bearing Fault Diagnosis. IEEE/ASME Transactions on Mechatronics. 28(3). 1695–1705. 28 indexed citations
12.
Wang, Daichao, Yibin Li, Yan Song, Lei Jia, & Tao Wen. (2022). Bearing Fault Diagnosis Method Based on Complementary Feature Extraction and Fusion of Multisensor Data. IEEE Transactions on Instrumentation and Measurement. 71. 1–10. 12 indexed citations
13.
Wang, Daichao, Yibin Li, Lei Jia, Yan Song, & Yanjun Liu. (2021). Novel Three-Stage Feature Fusion Method of Multimodal Data for Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 70. 1–10. 46 indexed citations
14.
Gao, Hui, et al.. (2021). A hybrid method based deep learning approach for Predicting residual Life of Machinery. 2021 China Automation Congress (CAC). 2. 4305–4309.
15.
Li, Yibin, et al.. (2019). Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network. IEEE Transactions on Industrial Informatics. 16(3). 2044–2053. 205 indexed citations breakdown →
16.
Wang, Daichao, et al.. (2019). Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis. Journal of Signal Processing Systems. 91(10). 1205–1217. 101 indexed citations
17.
Wang, Daichao, et al.. (2017). Automatic control system of agricultural machinery based on Beidou navigation. 318–323. 3 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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