Ao Ding

465 citations
18 papers · 267 · 1 hit paper · h-index 8

Impact in

Papers in

Ao Ding

14 papers receiving 258 citations

Ao Ding's Hit Papers

Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems 2024 · 74 citations
740+1Years since publication204060

Peers

Ao Ding
Comparison fields: 5 of 40
  • Control and Systems Engineering 161
  • Industrial and Manufacturing Engineering 34
  • Mechanical Engineering 99
  • Mechanics of Materials 41
  • Safety, Risk, Reliability and Quality 13
Replace Xun Dong with:
Xun Dong China
Feiyu Lu China
Zuogang Shang China
Qiubo Jiang China
Renhe Yao China
Xiaotian Zhang United Kingdom
Dongzhen Lyu China
Enhui Liu United States
Jin Uk Ko South Korea
Ao Ding relative to Xun Dong China Xun Dong's profile →
Citations per field
00.5×1.5×1.9×
Xun Dong · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Ao Ding, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ao Ding Line = papers co-authored together Ao Ding links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1
Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems
Hit paper breakdown →
202474
2 202250
3 202334
4 202331
5 202321
6 202211
7 202110
8 20248
9 20207
10 20246
11 20225
12 20225
13 20252
14 20232
15 20241
16 20250
17 20250
18 20250

About Ao Ding

Ao Ding is a scholar working on Control and Systems Engineering, Mechanical Engineering, Artificial Intelligence, Electrical and Electronic Engineering and Mechanics of Materials, having authored 18 papers that have together received 267 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (6 papers), Non-Destructive Testing Techniques (3 papers), Energy Harvesting in Wireless Networks (3 papers), Innovative Energy Harvesting Technologies (3 papers), Wireless Power Transfer Systems (3 papers), Industrial Vision Systems and Defect Detection (2 papers), Fault Detection and Control Systems (2 papers) and Vehicle License Plate Recognition (2 papers). The work is most often cited by research in Control and Systems Engineering (161 citations), Industrial and Manufacturing Engineering (34 citations), Mechanical Engineering (99 citations), Mechanics of Materials (41 citations) and Safety, Risk, Reliability and Quality (13 citations). Ao Ding has collaborated with scholars based in China, United Kingdom and Hong Kong. Frequent co-authors include Yong Qin, Biao Wang, Limin Jia, Xiaoqing Cheng, Liang Guo, Hailing Fu, Mengzhou Liu, Eric M. Yeatman, Lei Zhu and Hongfeng Li. Their work appears in journals such as Smart Materials and Structures, Measurement, IEEE Transactions on Intelligent Transportation Systems, Applied Energy and Engineering Applications of Artificial Intelligence.

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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