Mingkuan Shi

486 total citations
18 papers, 353 citations indexed

About

Mingkuan Shi is a scholar working on Control and Systems Engineering, Artificial Intelligence and Mechanics of Materials. According to data from OpenAlex, Mingkuan Shi has authored 18 papers receiving a total of 353 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Control and Systems Engineering, 9 papers in Artificial Intelligence and 6 papers in Mechanics of Materials. Recurrent topics in Mingkuan Shi's work include Machine Fault Diagnosis Techniques (13 papers), Engineering Diagnostics and Reliability (6 papers) and Machine Learning and ELM (6 papers). Mingkuan Shi is often cited by papers focused on Machine Fault Diagnosis Techniques (13 papers), Engineering Diagnostics and Reliability (6 papers) and Machine Learning and ELM (6 papers). Mingkuan Shi collaborates with scholars based in China and Australia. Mingkuan Shi's co-authors include Weiguo Huang, Changqing Shen, Chuancang Ding, Rui Wang, Zhongkui Zhu, Zhongkui Zhu, Jun Wang, Qiuyu Song, Wuyin Jin and Lingli Cui and has published in prestigious journals such as IEEE Transactions on Industrial Informatics, Reliability Engineering & System Safety and Knowledge-Based Systems.

In The Last Decade

Mingkuan Shi

18 papers receiving 341 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mingkuan Shi China 10 278 117 109 84 24 18 353
Jin Uk Ko South Korea 10 240 0.9× 115 1.0× 75 0.7× 69 0.8× 19 0.8× 14 305
Qiuyu Song China 9 396 1.4× 208 1.8× 78 0.7× 141 1.7× 20 0.8× 20 480
Andongzhe Duan China 7 218 0.8× 131 1.1× 79 0.7× 79 0.9× 11 0.5× 8 332
Francisco de Assis Boldt Brazil 7 347 1.2× 211 1.8× 93 0.9× 118 1.4× 43 1.8× 23 454
Yuan Sheng-fa China 5 281 1.0× 123 1.1× 77 0.7× 63 0.8× 14 0.6× 6 367
Daichao Wang China 7 351 1.3× 193 1.6× 67 0.6× 103 1.2× 29 1.2× 17 429
Yutong Dong China 11 291 1.0× 145 1.2× 78 0.7× 96 1.1× 16 0.7× 19 403
Zhilin Dong China 9 310 1.1× 172 1.5× 39 0.4× 87 1.0× 14 0.6× 18 378
Feibin Zhang China 11 397 1.4× 284 2.4× 43 0.4× 123 1.5× 17 0.7× 25 505
Linghui Meng China 8 165 0.6× 130 1.1× 78 0.7× 78 0.9× 46 1.9× 29 320

Countries citing papers authored by Mingkuan Shi

Since Specialization
Citations

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

Fields of papers citing papers by Mingkuan Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingkuan Shi

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

All Works

18 of 18 papers shown
1.
Ding, Chuancang, et al.. (2025). Class-aware quantitative adversarial network: a novel partial-set transfer mechanism for cross-domain fault diagnosis of rotating machinery. Knowledge-Based Systems. 328. 114259–114259. 1 indexed citations
2.
Zhao, Rongzhen, et al.. (2025). Multi-source contrastive cluster center method for cross-domain bearing fault identification. Engineering Applications of Artificial Intelligence. 161. 112056–112056. 1 indexed citations
3.
Zheng, Yuqiao, et al.. (2025). Multimetric hypergraph embedding for dimensionality reduction in rotor fault diagnosis. Advanced Engineering Informatics. 66. 103487–103487. 1 indexed citations
4.
Shi, Mingkuan, et al.. (2024). Granularity knowledge-sharing supervised contrastive learning framework for long-tailed fault diagnosis of rotating machinery. Knowledge-Based Systems. 301. 112354–112354. 3 indexed citations
5.
Shi, Mingkuan, et al.. (2024). Extended attention signal transformer with adaptive class imbalance loss for Long-tailed intelligent fault diagnosis of rotating machinery. Advanced Engineering Informatics. 60. 102436–102436. 29 indexed citations
6.
Shi, Mingkuan, Chuancang Ding, Rui Wang, et al.. (2024). Semi-supervised class incremental broad network for continuous diagnosis of rotating machinery faults with limited labeled samples. Knowledge-Based Systems. 286. 111397–111397. 11 indexed citations
7.
Shi, Mingkuan, Chuancang Ding, Changqing Shen, Weiguo Huang, & Zhongkui Zhu. (2024). Imbalanced class incremental learning system: A task incremental diagnosis method for imbalanced industrial streaming data. Advanced Engineering Informatics. 62. 102832–102832. 8 indexed citations
8.
Shi, Mingkuan, et al.. (2024). Cross-Domain Class Incremental Broad Network for Continuous Diagnosis of Rotating Machinery Faults Under Variable Operating Conditions. IEEE Transactions on Industrial Informatics. 20(4). 6356–6368. 43 indexed citations
9.
Wang, Rui, Weiguo Huang, Mingkuan Shi, Chuancang Ding, & Jun Wang. (2023). Multiweight Adversarial Open-Set Domain Adaptation Network for Machinery Fault Diagnosis With Unknown Faults. IEEE Sensors Journal. 23(24). 31483–31492. 10 indexed citations
10.
Shi, Mingkuan, Chuancang Ding, Rui Wang, et al.. (2023). Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery. Reliability Engineering & System Safety. 240. 109601–109601. 40 indexed citations
11.
Shi, Mingkuan, et al.. (2023). Cross-domain privacy-preserving broad network for fault diagnosis of rotating machinery. Advanced Engineering Informatics. 58. 102157–102157. 9 indexed citations
12.
Shi, Mingkuan, Chuancang Ding, Juanjuan Shi, et al.. (2022). Dimensionality reduction method based on similarity balance discriminant projection for bearing fault diagnosis. Measurement Science and Technology. 33(10). 105103–105103. 5 indexed citations
13.
Shi, Mingkuan, Chuancang Ding, Juanjuan Shi, et al.. (2022). Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing. Measurement. 207. 112299–112299. 21 indexed citations
14.
Wang, Rui, Weiguo Huang, Mingkuan Shi, et al.. (2022). Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy. Knowledge-Based Systems. 256. 109880–109880. 56 indexed citations
15.
Shi, Mingkuan, Chuancang Ding, Rui Wang, et al.. (2022). Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis. Knowledge-Based Systems. 260. 110172–110172. 48 indexed citations
16.
Shi, Mingkuan, et al.. (2020). Fault diagnosis of rotor based on Local-Global Balanced Orthogonal Discriminant Projection. Measurement. 168. 108320–108320. 36 indexed citations
17.
Sun, Weichen, Zhijing Zhang, Lingling Shi, et al.. (2020). Small sample parts recognition and localization from unfocused images in precision assembly systems using relative entropy. Precision Engineering. 68. 206–217. 4 indexed citations
18.
Zhao, Rongzhen, et al.. (2020). Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network. Applied Intelligence. 51(4). 2144–2160. 27 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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