World's Best Scientists 2026 revealed!
Junsheng Cheng

Junsheng Cheng

D-Index & Metrics

Engineering and Technology

D-Index
40
Citations
6714
World Ranking
7301
National Ranking
1328

Junsheng Cheng 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 Junsheng Cheng 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: 90 publications — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Junsheng Cheng 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 Junsheng Cheng 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: 40 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Junsheng Cheng is affiliated with Hunan University in China and works primarily in the field of Engineering. Their research focuses on several subfields, including Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials, Electrical and Electronic Engineering, and Molecular Biology.

The main topics of Junsheng Cheng's research include:

  • Machine Fault Diagnosis Techniques
  • Gear and Bearing Dynamics Analysis
  • Engineering Diagnostics and Reliability
  • Fault Detection and Control Systems
  • Structural Health Monitoring Techniques
  • Advanced Machining Processes and Optimization
  • Fuel Cells and Related Materials

Junsheng Cheng has published extensively in several key venues. Frequent publication venues include:

  • Measurement
  • Mechanical Systems and Signal Processing
  • Measurement Science and Technology
  • Mechanism and Machine Theory
  • IEEE/ASME Transactions on Mechatronics

Their notable recent papers include:

  • "Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information" (2022, IEEE/ASME Transactions on Mechatronics)
  • "Degradation prediction model for proton exchange membrane fuel cells based on long short-term memory neural network and Savitzky-Golay filter" (2021, International Journal of Hydrogen Energy)
  • "A noise reduction method based on adaptive weighted symplectic geometry decomposition and its application in early gear fault diagnosis" (2020, Mechanical Systems and Signal Processing)
  • "Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis" (2021, Mechanical Systems and Signal Processing)
  • "Data-driven flooding fault diagnosis method for proton-exchange membrane fuel cells using deep learning technologies" (2021, Energy Conversion and Management)

Junsheng Cheng has collaborated frequently with several researchers, reflecting a multidisciplinary approach to their work. Frequent co-authors include:

  • Yu Yang
  • Jian Cheng
  • Xin Li
  • Haidong Shao
  • Yanli Ma

Best Publications

  • Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings

    Dejie Yu;Junsheng Cheng;Yu Yang

  • A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

    Yu Yang;Dejie Yu;Junsheng Cheng

  • An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis

    Wenyi Huang;Junsheng Cheng;Yu Yang;Gaoyuan Guo

  • Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

    Jinde Zheng;Haiyang Pan;Junsheng Cheng

  • A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy

    Jinde Zheng;Junsheng Cheng;Yu Yang

  • Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis

    Haiyang Pan;Haiyang Pan;Yu Yang;Xin Li;Jinde Zheng

  • A rotating machinery fault diagnosis method based on local mean decomposition

    Unknown

  • Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing

    Jinde Zheng;Junsheng Cheng;Yu Yang

  • A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination

    Jinde Zheng;Junsheng Cheng;Yu Yang;Songrong Luo

  • Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

    Zhiyi He;Haidong Shao;Haidong Shao;Ping Wang;Janet (Jing) Lin

  • Application of support vector regression machines to the processing of end effects of Hilbert Huang transform

    Junsheng Cheng;Dejie Yu;Yu Yang

  • Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis

    Junsheng Cheng;Dejie Yu;Jiashi Tang;Yu Yang

  • Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information

    Unknown

  • Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

    Jinde Zheng;Haiyang Pan;Shubao Yang;Junsheng Cheng

  • An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems

    Yu Yang;Junsheng Cheng;Kang Zhang

  • Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis

    Dejie Yu;Yu Yang;Junsheng Cheng

  • Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis

    Jinde Zheng;Haiyang Pan;Shubao Yang;Junsheng Cheng

  • Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine

    Zhiyi He;Haidong Shao;Ziyang Ding;Hongkai Jiang

  • Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis

    Jinde Zheng;Junsheng Cheng;Yu Yang

  • Local rub-impact fault diagnosis of the rotor systems based on EMD

    Junsheng Cheng;Dejie Yu;Jiashi Tang;Yu Yang

  • Degradation prediction model for proton exchange membrane fuel cells based on long short-term memory neural network and Savitzky-Golay filter

    Unknown

  • A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach

    Yu Yang;Yigang He;Junsheng Cheng;Dejie Yu

  • An order tracking technique for the gear fault diagnosis using local mean decomposition method

    Junsheng Cheng;Kang Zhang;Yu Yang

  • Data-driven flooding fault diagnosis method for proton-exchange membrane fuel cells using deep learning technologies

    Unknown

Frequent Co-Authors

Haidong Shao
Haidong Shao Hunan University
Dejie Yu
Dejie Yu Hunan University
Kenli Li
Kenli Li Hunan University
Hongkai Jiang
Hongkai Jiang Northwestern Polytechnical University

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