World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
7376
World Ranking
5823
National Ranking
71

Zhi-Sheng Ye 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 Zhi-Sheng Ye 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: 157 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Zhi-Sheng Ye 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 Zhi-Sheng Ye 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: 44 D-Index — 42nd percentile

42% of scientists in this discipline score the same or lower.

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

Overview

Zhi-Sheng Ye is affiliated with the National University of Singapore. Their work primarily focuses on engineering, with an emphasis on statistics, probability, and reliability within various subfields of study.

Their research spans multiple specialized areas, including:

  • Reliability and Maintenance Optimization
  • Statistical Distribution Estimation and Applications
  • Fault Detection and Control Systems
  • Probabilistic and Robust Engineering Design
  • Machine Fault Diagnosis Techniques
  • Advanced Battery Technologies Research
  • Software Reliability and Analysis Research

Key subfields tracked in their work involve:

  • Statistics and Probability
  • Safety, Risk, Reliability and Quality
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Statistics, Probability and Uncertainty

Zhi-Sheng Ye has contributed extensively to several publication venues, notably:

  • Reliability Engineering & System Safety
  • arXiv (Cornell University)
  • Technometrics
  • IEEE Transactions on Reliability
  • IISE Transactions

Among their recent papers are:

  • "Bayesian deep-learning for RUL prediction: An active learning perspective" (2022), published in Reliability Engineering & System Safety
  • "A Condition Monitoring and Fault Isolation System for Wind Turbine Based on SCADA Data" (2021), published in IEEE Transactions on Industrial Informatics
  • "Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries" (2023), published in IEEE Transactions on Intelligent Vehicles
  • "A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines" (2022), published in Reliability Engineering & System Safety
  • "Joint Modeling of Degradation and Lifetime Data for RUL Prediction of Deteriorating Products" (2020), published in IEEE Transactions on Industrial Informatics

The frequent collaborators of Zhi-Sheng Ye include:

  • Qiuzhuang Sun
  • Xingchen Liu
  • Piao Chen
  • Jiawen Hu
  • Yang Yang

Best Publications

  • Stochastic modelling and analysis of degradation for highly reliable products

    Zhi-Sheng Ye;Min Xie

  • The Inverse Gaussian Process as a Degradation Model

    Zhi-Sheng Ye;Nan Chen

  • Degradation Data Analysis Using Wiener Processes With Measurement Errors

    Zhi-Sheng Ye;Yu Wang;Kwok-Leung Tsui;Michael Pecht

  • RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model

    Qingqing Zhai;Zhi-Sheng Ye

  • Condition-based maintenance using the inverse Gaussian degradation model

    Nan Chen;Zhi-Sheng Ye;Yisha Xiang;Linmiao Zhang

  • A new class of Wiener process models for degradation analysis

    Zhi-Sheng Ye;Nan Chen;Yan Shen

  • Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty

    Weiwen Peng;Zhi-Sheng Ye;Nan Chen

  • Accelerated Degradation Test Planning Using the Inverse Gaussian Process

    Zhi-Sheng Ye;Liang-Peng Chen;Loon Ching Tang;Min Xie

  • A two-phase preventive maintenance policy considering imperfect repair and postponed replacement

    Li Yang;Zhi-sheng Ye;Chi-Guhn Lee;Su-fen Yang

  • Bayesian deep-learning for RUL prediction: An active learning perspective

    Unknown

  • Degradation-based burn-in with preventive maintenance

    Zhi-Sheng Ye;Yan Shen;Min Xie

  • A Distribution-Based Systems Reliability Model Under Extreme Shocks and Natural Degradation

    Zhi Sheng Ye;Loon Ching Tang;Hai Yan Xu

  • Optimal Inspection and Replacement Policies for Multi-Unit Systems Subject to Degradation

    Qiuzhuang Sun;Zhi-Sheng Ye;Nan Chen

  • Semiparametric Estimation of Gamma Processes for Deteriorating Products

    Zhi-Sheng Ye;Min Xie;Loon-Ching Tang;Nan Chen

  • Designing Mission Abort Strategies Based on Early-Warning Information: Application to UAV

    Li Yang;Qiuzhuang Sun;Zhi-Sheng Ye

  • Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries

    Unknown

  • A Condition Monitoring and Fault Isolation System for Wind Turbine Based on SCADA Data

    Xingchen Liu;Juan Du;Zhi-Sheng Ye

  • Managing component degradation in series systems for balancing degradation through reallocation and maintenance

    Qiuzhuang Sun;Zhi-Sheng Ye;Xiaoyan Zhu

  • A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines

    Unknown

  • Warranty menu design for a two-dimensional warranty

    Zhi-Sheng Ye;D.N. Pra Murthy

  • Degradation-based burn-in planning under competing risks

    Zhi-Sheng Ye;Min Xie;Loon-Ching Tang;Yan Shen

  • Some improvements on adaptive genetic algorithms for reliability-related applications

    Zhisheng Ye;Zhizhong Li;Min Xie

  • Joint Online RUL Prediction for Multivariate Deteriorating Systems

    Weiwen Peng;Zhi-Sheng Ye;Nan Chen

  • Closed-Form Estimators for the Gamma Distribution Derived From Likelihood Equations

    Zhi-Sheng Ye;Nan Chen

  • Supplement to Degradation-Based Burn-In Planning Under Competing Risks

    Zhi-Sheng Ye;Min Xie;Yan Shen;Loon-Ching Tang

Frequent Co-Authors

Min Xie
Min Xie City University of Hong Kong
Loon Ching Tang
Loon Ching Tang National University of Singapore
Kwok-Leung Tsui
Kwok-Leung Tsui Virginia Tech
Hon Keung Tony Ng
Hon Keung Tony Ng Southern Methodist University
Michael Pecht
Michael Pecht University of Maryland, College Park
Yi Ding
Yi Ding Zhejiang University

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