World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
10371
World Ranking
4369
National Ranking
582

Chuan Li 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 Chuan Li 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: 161 publications — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Chuan Li 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 Chuan Li 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Chuan Li is affiliated with Chongqing Technology and Business University in China. Their research primarily falls within the field of Engineering, with significant contributions focused on Control and Systems Engineering, Mechanical Engineering, Electrical and Electronic Engineering, and Artificial Intelligence.

The main topics explored in their work include Machine Fault Diagnosis Techniques, Fault Detection and Control Systems, Gear and Bearing Dynamics Analysis, Industrial Vision Systems and Defect Detection, Anomaly Detection Techniques and Applications, Engineering Diagnostics and Reliability, and Climate Change and Health Impacts.

Chuan Li has published extensively in several venues, with a notable presence in:

  • SSRN Electronic Journal
  • Mechanical Systems and Signal Processing
  • Measurement
  • Measurement Science and Technology
  • arXiv (Cornell University)

Frequent collaborators in their work include Jianyu Long, Yun Bai, Diego Cabrera, Zhe Yang, and Shuai Yang.

Among Chuan Li's recent papers are:

  • "A systematic review of deep transfer learning for machinery fault diagnosis" (2020), published in Neurocomputing
  • "Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots" (2020), published in Journal of Manufacturing Systems
  • "Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring" (2021), published in Mechanical Systems and Signal Processing
  • "A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions" (2022), published in Engineering Applications of Artificial Intelligence
  • "Box-Cox sparse measures: A new family of sparse measures constructed from kurtosis and negative entropy" (2021), published in Mechanical Systems and Signal Processing

Best Publications

  • Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    Chuan Li;Chuan Li;René Vinicio Sanchez;Grover Zurita;Mariela Cerrada

  • State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network

    Fangfang Yang;Fangfang Yang;Weihua Li;Chuan Li;Qiang Miao

  • Gearbox Fault Identification and Classification with Convolutional Neural Networks

    ZhiQiang Chen;Chuan Li;René-Vinicio Sanchez

  • A systematic review of deep transfer learning for machinery fault diagnosis

    Chuan Li;Shaohui Zhang;Yi Qin;Edgar Estupinan

  • Fault diagnosis in spur gears based on genetic algorithm and random forest

    Mariela Cerrada;Mariela Cerrada;Grover Zurita;Diego Cabrera;René Vinicio Sánchez

  • Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

    Yun Bai;Yong Li;Xiaoxue Wang;Jingjing Xie

  • Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform

    Chuan Li;Chuan Li;Ming Liang

  • Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

    Chuan Li;René-Vinicio Sanchez;Grover Zurita;Mariela Cerrada

  • Deep neural networks-based rolling bearing fault diagnosis

    Zhiqiang Chen;Shengcai Deng;Xudong Chen;Chuan Li

  • Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    Chuan Li;René Vinicio Sánchez;Grover Zurita;Mariela Cerrada

  • A generalized synchrosqueezing transform for enhancing signal time-frequency representation

    Chuan Li;Ming Liang

  • Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models

    Yun Bai;Zhiqiang Chen;Jingjing Xie;Chuan Li

  • Forecasting the natural gas demand in China using a self-adapting intelligent grey model

    Bo Zeng;Chuan Li

  • Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition

    Chuan Li;Chuan Li;Ying Tao;Wengang Ao;Shuai Yang

  • Evolving Deep Echo State Networks for Intelligent Fault Diagnosis

    Jianyu Long;Shaohui Zhang;Chuan Li

  • An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting.

    Yun Bai;Bo Zeng;Chuan Li;Jin Zhang

  • Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application

    Bo Zeng;Chuan Li

  • Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals

    Chuan Li;Chuan Li;Ming Liang;Tianyang Wang

  • Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time–frequency ridge enhancement

    Chuan Li;Chuan Li;Vinicio Sanchez;Grover Zurita;Mariela Cerrada Lozada

  • A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis

    Chuan Li;Jose Valente de Oliveira;Mariela Cerrada;Diego Cabrera

  • Development of an optimization method for the GM(1,N) model

    Bo Zeng;Chengming Luo;Sifeng Liu;Yun Bai

Frequent Co-Authors

Ming Liang
Ming Liang University of Ottawa
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Sifeng Liu
Sifeng Liu Nanjing University of Aeronautics and Astronautics
Michael Pecht
Michael Pecht University of Maryland, College Park
Panos M. Pardalos
Panos M. Pardalos University of Florida
Zhirong Zhang
Zhirong Zhang Sichuan University
Rui Xiong
Rui Xiong Beijing Institute of Technology
Jiuchun Jiang
Jiuchun Jiang Hubei University of Technology
Sheng-Nian Luo
Sheng-Nian Luo Southwest Jiaotong University
Minhao Zhu
Minhao Zhu Southwest Jiaotong University

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