Yaowei Shi

606 total citations · 1 hit paper
28 papers, 469 citations indexed

About

Yaowei Shi is a scholar working on Control and Systems Engineering, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Yaowei Shi has authored 28 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Control and Systems Engineering, 15 papers in Mechanical Engineering and 7 papers in Artificial Intelligence. Recurrent topics in Yaowei Shi's work include Machine Fault Diagnosis Techniques (18 papers), Non-Destructive Testing Techniques (10 papers) and Engineering Diagnostics and Reliability (6 papers). Yaowei Shi is often cited by papers focused on Machine Fault Diagnosis Techniques (18 papers), Non-Destructive Testing Techniques (10 papers) and Engineering Diagnostics and Reliability (6 papers). Yaowei Shi collaborates with scholars based in China, Bangladesh and United States. Yaowei Shi's co-authors include Minqiang Deng, Aidong Deng, Xue Ding, Meng Xu, Yang Liu, Jing Li, Aidong Deng, Shun Zhang, Shuo Xu and Meng Xu and has published in prestigious journals such as PLoS ONE, Journal of Cleaner Production and IEEE Access.

In The Last Decade

Yaowei Shi

24 papers receiving 457 citations

Hit Papers

Domain augmentation generalization network for real-time ... 2023 2026 2024 2025 2023 25 50 75

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaowei Shi China 12 369 181 121 112 43 28 469
Sonal Dixit India 10 291 0.8× 206 1.1× 87 0.7× 83 0.7× 54 1.3× 22 464
Chenhui Qian China 7 243 0.7× 124 0.7× 63 0.5× 87 0.8× 30 0.7× 15 323
Jorge Nei Brito Brazil 6 278 0.8× 139 0.8× 89 0.7× 90 0.8× 57 1.3× 15 387
Hongbo Ma China 13 333 0.9× 229 1.3× 65 0.5× 123 1.1× 39 0.9× 38 495
Shengli Zhang United States 4 325 0.9× 231 1.3× 66 0.5× 141 1.3× 30 0.7× 8 458
Wenjing Duan China 4 474 1.3× 317 1.8× 51 0.4× 187 1.7× 37 0.9× 6 543
Gang Mao China 10 230 0.6× 147 0.8× 53 0.4× 74 0.7× 22 0.5× 22 344
Ali Dibaj Iran 5 302 0.8× 208 1.1× 32 0.3× 131 1.2× 38 0.9× 8 358
Shaodong Zheng China 9 191 0.5× 118 0.7× 54 0.4× 26 0.2× 18 0.4× 12 311

Countries citing papers authored by Yaowei Shi

Since Specialization
Citations

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

Fields of papers citing papers by Yaowei Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaowei Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Yaowei Shi. A scholar is included among the top collaborators of Yaowei Shi 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 Yaowei Shi. Yaowei Shi 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.
Xu, Meng, et al.. (2025). A class confusion-aware spherical network approach for closed-set domain adaptation in rotating machinery fault diagnosis. Measurement Science and Technology. 36(8). 86208–86208. 1 indexed citations
2.
Deng, Minqiang, et al.. (2025). Universal domain adaptation in rotating machinery fault diagnosis: A self-supervised orthogonal clustering approach. Reliability Engineering & System Safety. 257. 110828–110828. 2 indexed citations
3.
Deng, Aidong, et al.. (2024). A Fuzzy Confusion Matrix-Based Self-Supervised Learning Method to Mitigate Class Confusion for Partial Transfer Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 73. 1–12. 3 indexed citations
4.
Ding, Xue, Aidong Deng, Minqiang Deng, Dongying Liu, & Yaowei Shi. (2024). Mitigating Decision Boundary Confusion: A Classifier Prediction-Oriented Domain Adaptation Network. IEEE Sensors Journal. 24(11). 18672–18684. 1 indexed citations
6.
Deng, Aidong, et al.. (2023). Transforming the Open Set Into a Pseudo-Closed Set: A Regularized GAN for Domain Adaptation in Open-Set Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 72. 1–12. 16 indexed citations
7.
Shi, Yaowei, Aidong Deng, Minqiang Deng, et al.. (2023). Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions. Reliability Engineering & System Safety. 235. 109188–109188. 84 indexed citations breakdown →
8.
Shi, Yaowei, et al.. (2023). An improved multi-scale branching convolutional neural network for rolling bearing fault diagnosis. PLoS ONE. 18(9). e0291353–e0291353. 5 indexed citations
9.
Shi, Yaowei, Aidong Deng, Meng Xu, & Minqiang Deng. (2023). Adaptive Mixup-Based Domain Adaptation Method for Intelligent Fault Diagnosis. 296–300.
10.
Deng, Minqiang, Aidong Deng, Yaowei Shi, Yang Liu, & Meng Xu. (2022). A novel sub-label learning mechanism for enhanced cross-domain fault diagnosis of rotating machinery. Reliability Engineering & System Safety. 225. 108589–108589. 22 indexed citations
11.
Shi, Yaowei, Aidong Deng, Minqiang Deng, et al.. (2022). Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis. Reliability Engineering & System Safety. 226. 108684–108684. 39 indexed citations
12.
Shi, Yaowei, Aidong Deng, Minqiang Deng, et al.. (2022). Domain Transferability-Based Deep Domain Generalization Method Towards Actual Fault Diagnosis Scenarios. IEEE Transactions on Industrial Informatics. 19(6). 7355–7366. 53 indexed citations
13.
Deng, Minqiang, Aidong Deng, Yaowei Shi, & Meng Xu. (2022). Correlation Regularized Conditional Adversarial Adaptation for Multi-Target-Domain Fault Diagnosis. IEEE Transactions on Industrial Informatics. 18(12). 8692–8702. 33 indexed citations
14.
Shi, Yaowei, Aidong Deng, Minqiang Deng, et al.. (2022). A novel multiscale feature adversarial fusion network for unsupervised cross-domain fault diagnosis. Measurement. 200. 111616–111616. 10 indexed citations
15.
Deng, Minqiang, Aidong Deng, Yaowei Shi, Yang Liu, & Meng Xu. (2022). Intelligent fault diagnosis based on sample weighted joint adversarial network. Neurocomputing. 488. 168–182. 14 indexed citations
16.
Deng, Minqiang, Aidong Deng, & Yaowei Shi. (2021). Self-supervised Adversarial Network for Intelligent Fault Diagnosis. 323–326. 1 indexed citations
17.
Deng, Minqiang, et al.. (2020). Resonance-based bandwidth Fourier decomposition method for gearbox fault diagnosis. Measurement Science and Technology. 32(3). 35003–35003. 7 indexed citations
18.
Shi, Yaowei, et al.. (2020). Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis. IEEE Access. 8. 217723–217734. 29 indexed citations
19.
Zhan, Cheng, et al.. (2015). The development and application of digital PCR. 1 indexed citations
20.
Shi, Yaowei, Feng Wu, Yongjia Zheng, et al.. (1994). Optical active process of higher order bands in fast neutron irradiated silicon. Solid State Communications. 91(8). 631–634.

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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