World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
84
Citations
30561
World Ranking
847
National Ranking
125

Ruqiang Yan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Ruqiang Yan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 348 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Ruqiang Yan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Ruqiang Yan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 84 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2019 - Fellow of the American Society of Mechanical Engineers

Overview

Ruqiang Yan is affiliated with Xi'an Jiaotong University in China, contributing extensively to the field of engineering with a focus on control and systems engineering. Their work spans multiple subfields including mechanical engineering, artificial intelligence, electrical and electronic engineering, and civil and structural engineering. The principal area of research covers machine fault diagnosis techniques alongside fault detection and control systems.

The scientist's recent publications examine advanced methodologies in fault diagnosis and prognostics within industrial scenarios. Notable papers include:

  • A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges (2021, Mechanical Systems and Signal Processing)
  • Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study (2020, ISA Transactions)
  • Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2020, IEEE Transactions on Industrial Electronics)
  • The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study (2021, Mechanical Systems and Signal Processing)
  • Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2021, IEEE Transactions on Instrumentation and Measurement)

The scientist frequently collaborates with several researchers, including Xuefeng Chen, Chuang Sun, Zhibin Zhao, Jiawen Xu, and Salvatore Baglio.

Their publication record spans a range of prominent venues related to instrumentation and measurement as well as mechanical systems and industrial informatics, such as:

  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Instrumentation & Measurement Magazine
  • Mechanical Systems and Signal Processing
  • IEEE Transactions on Industrial Informatics
  • Journal of Manufacturing Systems

Research topics frequently addressed include:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Anomaly Detection Techniques and Applications
  • Structural Health Monitoring Techniques
  • Non-Destructive Testing Techniques
  • Industrial Vision Systems and Defect Detection

Ruqiang Yan has been recognized with the designation of Fellow of the American Society of Mechanical Engineers in 2019.

Best Publications

  • Deep learning and its applications to machine health monitoring

    Rui Zhao;Ruqiang Yan;Zhenghua Chen;Kezhi Mao

  • Wavelets for fault diagnosis of rotary machines: A review with applications

    Ruqiang Yan;Robert X. Gao;Xuefeng Chen

  • Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

    Siyu Shao;Stephen McAleer;Ruqiang Yan;Pierre Baldi

  • Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

    Rui Zhao;Dongzhe Wang;Ruqiang Yan;Kezhi Mao

  • Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

    Rui Zhao;Ruqiang Yan;Jinjiang Wang;Kezhi Mao

  • A sparse auto-encoder-based deep neural network approach for induction motor faults classification

    Wenjun Sun;Siyu Shao;Rui Zhao;Ruqiang Yan;Ruqiang Yan

  • A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

    Weihua Li;Ruyi Huang;Jipu Li;Yixiao Liao

  • Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study.

    Zhibin Zhao;Tianfu Li;Jingyao Wu;Chuang Sun

  • Approximate Entropy as a diagnostic tool for machine health monitoring

    Ruqiang Yan;Robert X. Gao

  • Wavelets: Theory and Applications for Manufacturing

    Robert X. Gao;Ruqiang Yan

  • Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach

    Zhenghua Chen;Min Wu;Rui Zhao;Feri Guretno

  • Generative adversarial networks for data augmentation in machine fault diagnosis

    Siyu Shao;Pu Wang;Ruqiang Yan;Ruqiang Yan

  • Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing

    Chuang Sun;Meng Ma;Zhibin Zhao;Shaohua Tian

  • WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

    Tianfu Li;Zhibin Zhao;Chuang Sun;Li Cheng

  • Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

    Zhibin Zhao;Qiyang Zhang;Xiaolei Yu;Chuang Sun

  • Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring

    Ruqiang Yan;R.X. Gao

  • Long short-term memory for machine remaining life prediction

    Jianjing Zhang;Peng Wang;Ruqiang Yan;Robert X. Gao

  • Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines

    Ruqiang Yan;Yongbin Liu;Robert X. Gao

  • DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

    Siyu Shao;Ruqiang Yan;Yadong Lu;Peng Wang

  • Prognosis of Defect Propagation Based on Recurrent Neural Networks

    A Malhi;Ruqiang Yan;R X Gao

  • Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis

    Tianfu Li;Zhibin Zhao;Chuang Sun;Ruqiang Yan

Frequent Co-Authors

Robert X. Gao
Robert X. Gao Case Western Reserve University
Xuefeng Chen
Xuefeng Chen Xi'an Jiaotong University
Xingwu Zhang
Xingwu Zhang Xi'an Jiaotong University
Kezhi Mao
Kezhi Mao Nanyang Technological University
Hongrui Cao
Hongrui Cao Xi'an Jiaotong University
Qingbo He
Qingbo He Shanghai Jiao Tong University
Xiaoli Li
Xiaoli Li Singapore University of Technology and Design
Lihui Wang
Lihui Wang Royal Institute of Technology
Fanrang Kong
Fanrang Kong University of Science and Technology of China
Reza Langari
Reza Langari Texas A&M University

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