Dechen Yao

1.6k total citations
82 papers, 1.2k citations indexed

About

Dechen Yao is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Dechen Yao has authored 82 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Control and Systems Engineering, 37 papers in Mechanical Engineering and 18 papers in Mechanics of Materials. Recurrent topics in Dechen Yao's work include Machine Fault Diagnosis Techniques (40 papers), Gear and Bearing Dynamics Analysis (28 papers) and Engineering Diagnostics and Reliability (14 papers). Dechen Yao is often cited by papers focused on Machine Fault Diagnosis Techniques (40 papers), Gear and Bearing Dynamics Analysis (28 papers) and Engineering Diagnostics and Reliability (14 papers). Dechen Yao collaborates with scholars based in China, United States and Canada. Dechen Yao's co-authors include Jianwei Yang, Hengchang Liu, M.R. Azimi-Sadjadi, G.J. Dobeck, Xi Li, Qiang Huang, Tangbo Bai, Jinhai Wang, Limin Jia and Boyang Li and has published in prestigious journals such as IEEE Access, Sensors and Reliability Engineering & System Safety.

In The Last Decade

Dechen Yao

77 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dechen Yao China 18 548 457 207 163 148 82 1.2k
Tariq Sattar United Kingdom 15 232 0.4× 251 0.5× 124 0.6× 39 0.2× 136 0.9× 75 956
Wenhua Du China 19 1.2k 2.2× 851 1.9× 369 1.8× 18 0.1× 202 1.4× 44 1.6k
Yan Han China 12 455 0.8× 270 0.6× 137 0.7× 21 0.1× 81 0.5× 31 822
F.L. Chu China 12 1.4k 2.6× 979 2.1× 542 2.6× 28 0.2× 548 3.7× 18 2.0k
Andrea Coraddu Netherlands 22 365 0.7× 234 0.5× 199 1.0× 54 0.3× 114 0.8× 106 1.7k
Daniel Toal Ireland 21 447 0.8× 284 0.6× 25 0.1× 115 0.7× 57 0.4× 136 1.9k
Xiaoyuan Zhang China 17 1.1k 1.9× 649 1.4× 406 2.0× 8 0.0× 121 0.8× 47 1.5k
Aijun Hu China 23 1.3k 2.4× 783 1.7× 361 1.7× 11 0.1× 293 2.0× 80 2.0k
Murat Aşkar Türkiye 8 813 1.5× 449 1.0× 272 1.3× 6 0.0× 118 0.8× 31 1.3k

Countries citing papers authored by Dechen Yao

Since Specialization
Citations

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

Fields of papers citing papers by Dechen Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dechen Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Dechen Yao. A scholar is included among the top collaborators of Dechen Yao 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 Dechen Yao. Dechen Yao 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.
Ying, Ren, et al.. (2025). Vibration reduction and dynamic response analysis of urban railway steel-spring floating slabs under periodic track irregularities. Journal of Vibration and Control. 32(1-2). 269–281. 1 indexed citations
2.
Wang, Ning, et al.. (2025). Dual-stage manifold preserving mixed supervised learning for bogie fault diagnosis under variable conditions. Engineering Applications of Artificial Intelligence. 149. 110512–110512. 1 indexed citations
3.
Wang, Jinhai, et al.. (2025). A Real-Time Polygonal Wheel-Rail Force Identification Method Based on Convolutional Neural Networks (CNN). Urban Rail Transit. 11(2). 178–194. 1 indexed citations
4.
Wang, Jinhai, et al.. (2025). Supervised Contrastive Learning Enhanced Deep Residual Shrinkage Network for Dual Uncertainty-Aware Bearing RUL Prediction. IEEE Sensors Journal. 25(19). 36254–36266. 3 indexed citations
5.
Yang, Jianwei, et al.. (2024). Short-time adaptive compact kernel distribution for fault diagnosis under variable working condition bearing. Journal of Vibration and Control. 31(23-24). 5011–5023. 1 indexed citations
6.
Yang, Jianwei, et al.. (2024). Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition. Measurement Science and Technology. 35(7). 76102–76102. 5 indexed citations
7.
Wang, Xuan, et al.. (2024). Dynamic characteristics of electromechanical coupling of body-suspended drive system for high-speed trains under wheel polygonal wear. Transactions of the Canadian Society for Mechanical Engineering. 48(4). 659–670. 1 indexed citations
8.
Yao, Dechen, et al.. (2024). Multiscale PatchTCN-Mixer: A New Method for Extracting Spatial and Temporal Degradation Information in Remaining Useful Life Prognosis. IEEE Sensors Journal. 24(15). 23537–23550. 3 indexed citations
9.
Wang, Jinhai, et al.. (2024). Adaptive MAGNN-TCN: An Innovative Approach for Bearings Remaining Useful Life Prediction. IEEE Sensors Journal. 25(4). 7467–7481. 1 indexed citations
10.
Yao, Dechen, et al.. (2024). Remaining Useful Life Prognosis Method of Rolling Bearings Considering Degradation Distribution Shift. IEEE Transactions on Instrumentation and Measurement. 73. 1–13. 18 indexed citations
11.
Yang, Jianwei, et al.. (2024). Compound Fault Diagnosis in Railway Vehicle Wheelset Bearing Based on ISAM-AHKD. IEEE Transactions on Instrumentation and Measurement. 74. 1–13. 1 indexed citations
12.
Yang, Jianwei, et al.. (2023). Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA. Measurement Science and Technology. 35(2). 26107–26107. 2 indexed citations
13.
Bai, Tangbo, et al.. (2021). Information Fusion of Infrared Images and Vibration Signals for Coupling Fault Diagnosis of Rotating Machinery. Shock and Vibration. 2021(1). 17 indexed citations
14.
Bai, Tangbo, Jialin Gao, Jianwei Yang, & Dechen Yao. (2021). A Study on Railway Surface Defects Detection Based on Machine Vision. Entropy. 23(11). 1437–1437. 41 indexed citations
15.
Yao, Dechen, et al.. (2021). An intelligent method of roller bearing fault diagnosis and fault characteristic frequency visualization based on improved MobileNet V3. Measurement Science and Technology. 32(12). 124009–124009. 20 indexed citations
16.
Wang, Jinhai, et al.. (2020). A comparative study of the vibration characteristics of railway vehicle axlebox bearings with inner/outer race faults. Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit. 235(8). 1035–1047. 20 indexed citations
17.
Yao, Dechen, et al.. (2013). Harmonic W avelet Envelope Method Applied in Railway Bearing Fault Diagnosis. Journal of Engineering Science and Technology Review. 6(2). 24–28. 3 indexed citations
18.
Yao, Dechen. (2010). Fault Diagnosis of Railway Bearing Based on Muti-method Fusion Techniques. Machine Design and Research. 2 indexed citations
19.
Yao, Dechen. (2010). Fault Diagnosis of Bearing Based on Improved Wavelet Packet and RBF Neural Network. Machine Design and Research. 2 indexed citations
20.
Azimi-Sadjadi, M.R., Dechen Yao, Qiang Huang, & G.J. Dobeck. (2000). Underwater target classification using wavelet packets and neural networks. IEEE Transactions on Neural Networks. 11(3). 784–794. 155 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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