Ao Ding

465 total citations · 1 hit paper
18 papers, 267 citations indexed

About

Ao Ding is a scholar working on Control and Systems Engineering, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Ao Ding has authored 18 papers receiving a total of 267 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Control and Systems Engineering, 8 papers in Mechanical Engineering and 6 papers in Artificial Intelligence. Recurrent topics in Ao Ding's work include Machine Fault Diagnosis Techniques (6 papers), Energy Harvesting in Wireless Networks (3 papers) and Non-Destructive Testing Techniques (3 papers). Ao Ding is often cited by papers focused on Machine Fault Diagnosis Techniques (6 papers), Energy Harvesting in Wireless Networks (3 papers) and Non-Destructive Testing Techniques (3 papers). Ao Ding collaborates with scholars based in China, United Kingdom and United States. Ao Ding's co-authors include Yong Qin, Biao Wang, Limin Jia, Xiaoqing Cheng, Liang Guo, Mengzhou Liu, Hailing Fu, Eric M. Yeatman, Lei Zhu and Lei Huang and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Applied Energy and IEEE Access.

In The Last Decade

Ao Ding

14 papers receiving 258 citations

Hit Papers

align trajectories

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.

Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems

2024 74 citations Ao Ding, Yong Qin et al. Mechanical Systems and Signal Processing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ao Ding China	8	161	99	67	41	40	18	267
Dongzhen Lyu China	8	216 1.3×	100 1.0×	86 1.3×	52 1.3×	26 0.7×	19	325
Qiubo Jiang China	7	137 0.9×	120 1.2×	43 0.6×	56 1.4×	19 0.5×	14	263
Haizhou Huang China	7	239 1.5×	177 1.8×	33 0.5×	78 1.9×	43 1.1×	16	327
Zuogang Shang China	7	232 1.4×	106 1.1×	87 1.3×	60 1.5×	73 1.8×	16	351
Yuekai Liu China	10	172 1.1×	212 2.1×	101 1.5×	51 1.2×	67 1.7×	11	346
Feiyu Lu China	11	239 1.5×	99 1.0×	33 0.5×	62 1.5×	61 1.5×	26	297
Yunhan Kim South Korea	13	267 1.7×	214 2.2×	37 0.6×	65 1.6×	27 0.7×	15	377
Hongdi Zhou China	11	202 1.3×	222 2.2×	87 1.3×	92 2.2×	32 0.8×	27	369
S. Ravikumar India	8	96 0.6×	173 1.7×	65 1.0×	26 0.6×	32 0.8×	15	298
Vilas N. Ghate India	8	257 1.6×	125 1.3×	116 1.7×	86 2.1×	47 1.2×	23	354

Countries citing papers authored by Ao Ding

Since Specialization

Citations

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

Fields of papers citing papers by Ao Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ao Ding

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

All Works

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18 of 18 papers shown

Wang, Biao, et al.. (2025). An Interpretable Few-Shot Framework for Fault Diagnosis of Train Transmission Systems with Noisy Labels.

Qin, Yong, Zhaojun Li, Biao Wang, et al.. (2025). An In-Depth Tutorial on BJTU-RAO Bogie Datasets for Fault Diagnosis. IEEE Access. 13. 60879–60888. 2 indexed citations

Ding, Ao, et al.. (2025). Multi-agent reinforcement learning system framework based on topological networks in Fourier space. Applied Soft Computing. 174. 112986–112986.

Wang, Tianhao, et al.. (2025). A Heuristic Fast Task Allocation Algorithm for War Game Simulation Scenarios. 2328–2333.

Wang, Wenyuan, et al.. (2024). Deep Reinforcement Learning for Channel Traffic Scheduling in Dry Bulk Export Terminals. IEEE Transactions on Intelligent Transportation Systems. 25(11). 17547–17561. 6 indexed citations

Ding, Ao, Yong Qin, Biao Wang, et al.. (2024). Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems. Mechanical Systems and Signal Processing. 210. 111175–111175. 74 indexed citations breakdown →

Ding, Ao, Yong Qin, Biao Wang, et al.. (2024). Brownian Distance Covariance-Based Few-Shot Learning Framework Considering Noisy Labels for Fault Diagnosis of Train Transmission Systems. IEEE Transactions on Industrial Informatics. 21(1). 136–145. 8 indexed citations

Li, Gaolei, et al.. (2024). RepFTI: Representation-Fused Function-Type Inference for Vehicular Secure Software Systems. Applied Sciences. 14(11). 4502–4502. 1 indexed citations

Liu, Mengzhou, et al.. (2023). A seesaw-inspired bistable energy harvester with adjustable potential wells for self-powered internet of train monitoring. Applied Energy. 337. 120908–120908. 31 indexed citations

10.

Ding, Ao, et al.. (2023). Self-driven continual learning for class-added motor fault diagnosis based on unseen fault detector and propensity distillation. Engineering Applications of Artificial Intelligence. 127. 107382–107382. 21 indexed citations

11.

Ding, Ao, Yong Qin, Biao Wang, Xiaoqing Cheng, & Limin Jia. (2023). An Elastic Expandable Fault Diagnosis Method of Three-Phase Motors Using Continual Learning for Class-Added Sample Accumulations. IEEE Transactions on Industrial Electronics. 71(7). 7896–7905. 34 indexed citations

12.

Ding, Ao, Yong Qin, Biao Wang, & Limin Jia. (2023). A Class-Added Continual Learning Method for Motor Fault Diagnosis Based on Knowledge Distillation of Representation Proximity Behavior. 224–231. 2 indexed citations

13.

Qin, Yong, et al.. (2022). A bistable energy harvester with low base-acceleration and high root mean square output for train bogies: theoretical modeling and experimental validation. Smart Materials and Structures. 32(3). 35009–35009. 5 indexed citations

14.

Ding, Ao, Yong Qin, Biao Wang, Limin Jia, & Xiaoqing Cheng. (2022). Lightweight Multiscale Convolutional Networks With Adaptive Pruning for Intelligent Fault Diagnosis of Train Bogie Bearings in Edge Computing Scenarios. IEEE Transactions on Instrumentation and Measurement. 72. 1–13. 50 indexed citations

15.

Fu, Hailing, et al.. (2022). A bistable vibration energy harvester with spherical moving magnets: Theoretical modeling and experimental validation. Sensors and Actuators A Physical. 345. 113782–113782. 11 indexed citations

16.

Wang, Rui, Shunjie Zhang, Shengqiang Liu, Weidong Liu, & Ao Ding. (2022). A bearing fault diagnosis method for high-noise and unbalanced dataset. 5(1). 28–45. 5 indexed citations

17.

Ding, Ao, et al.. (2021). Intelligent recognition of rough handling of express parcels based on CNN-GRU with the channel attention mechanism. Journal of Ambient Intelligence and Humanized Computing. 14(2). 973–990. 10 indexed citations

18.

Ding, Ao, et al.. (2020). Recognition method research on rough handling of express parcels based on acceleration features and CNN. Measurement. 163. 107942–107942. 7 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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