World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
46
Citations
7622
World Ranking
5195
National Ranking
1471

David He publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where David He sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 170 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

David He D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where David He sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 46 D-Index — 49th percentile

49% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

David He is affiliated with the University of Illinois at Chicago in the United States. Their research primarily focuses on engineering, with significant contributions in control and systems engineering, mechanical engineering, mechanics of materials, civil and structural engineering, and ocean engineering.

Their research topics include:

  • Machine Fault Diagnosis Techniques
  • Gear and Bearing Dynamics Analysis
  • Fault Detection and Control Systems
  • Engineering Diagnostics and Reliability
  • Mechanical Failure Analysis and Simulation
  • Structural Health Monitoring Techniques
  • Iterative Learning Control Systems

David He has published extensively in several venues, notably:

  • International Journal of Prognostics and Health Management
  • Annual Conference of the PHM Society
  • Journal of Intelligent Manufacturing
  • PHM Society Asia-Pacific Conference
  • IADC/SPE International Drilling Conference and Exhibition

Some recent papers demonstrate the breadth of David He's research:

  • Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection, 2020, Mechanical Systems and Signal Processing
  • Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering, 2020, International Journal of Prognostics and Health Management
  • Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor, 2020, Journal of Intelligent Manufacturing
  • Dynamic characteristics and reliability analysis of ball screw feed system on a lathe, 2020, Mechanism and Machine Theory
  • Time-dependent nonlinear dynamic model for linear guideway with crowning, 2020, Tribology International

Frequent co-authors collaborating with David He include:

  • Eric Bechhoefer
  • Yongzhi Qu
  • Xueyi Li
  • Jialin Li
  • Miao He

The collective work of David He reflects a focus on fault diagnosis, reliability analysis, and advanced control strategies within mechanical systems. Their contributions span numerous areas within engineering diagnostics and mechanical system performance monitoring.

Best Publications

  • Deep Learning Based Approach for Bearing Fault Diagnosis

    Miao He;David He

  • Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components

    Jason Deutsch;David He

  • A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

    Ming Dong;David He

  • Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

    Ming Dong;David He

  • A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

    Jialin Li;Xueyi Li;David He

  • Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification

    Ruoyu Li;D. He

  • PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining

    Ming Dong;Dong Yang;Yan Kuang;David He

  • Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

    Unknown

  • Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors--a comparative study.

    Yongzhi Qu;David He;Jae Yoon;Brandon Van Hecke

  • Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach

    D. He;Ruoyu Li;Junda Zhu

  • Design of assembly systems for modular products

    D.W. He;A. Kusiak

  • Low speed bearing fault diagnosis using acoustic emission sensors

    Brandon Van Hecke;Jae Yoon;David He

  • Lithium-ion battery life prognostic health management system using particle filtering framework

    M Dalal;J Ma;D He

  • Design of double- and triple-sampling X-bar control charts using genetic algorithms

    D. He;A. Grigoryan;M. Sigh

  • Equipment health diagnosis and prognosis using hidden semi-Markov models

    Ming Dong;David He;Prashant Banerjee;Jonathan Keller

  • Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering

    Junda Zhu;Jae M. Yoon;David He;Yongzhi Qu

  • Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines

    Junda Zhu;Jae M. Yoon;David He;Eric Bechhoefer

  • Fault features extraction for bearing prognostics

    Ruoyu Li;Ponrit Sopon;David He

  • Analysis of sequential failures for assessment of reliability and safety of manufacturing systems

    Angela Adamyan;David He

  • A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals

    Miao He;David He

  • A survey of lubrication oil condition monitoring, diagnostics and prognostics techniques and systems

    Junda Zhu;David He;Eric Bechhoefer

  • Design for agile assembly: An operational perspective

    Andrew Kusiak;D. W. He

Frequent Co-Authors

Andrew Kusiak
Andrew Kusiak University of Iowa
Abhinav Saxena
Abhinav Saxena General Electric (United States)
Zude Zhou
Zude Zhou Wuhan University of Technology

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