World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
41
Citations
7107
World Ranking
6941
National Ranking
1275

Ming Zhao 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 Ming Zhao 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: 81 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

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

Ming Zhao 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 Ming Zhao 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: 41 D-Index — 31st percentile

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

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

Overview

Ming Zhao is affiliated with Xi'an Jiaotong University in China and has a substantial record of research focusing on engineering. Their work primarily spans several subfields including Control and Systems Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Civil and Structural Engineering, and Computer Vision and Pattern Recognition.

Their research portfolio includes 166 publications within engineering, with a particular emphasis on the following main topics:

  • Machine Fault Diagnosis Techniques
  • Gear and Bearing Dynamics Analysis
  • Fault Detection and Control Systems
  • Structural Health Monitoring Techniques
  • Advanced machining processes and optimization
  • Engineering Diagnostics and Reliability
  • Non-Destructive Testing Techniques

Recent papers authored in collaboration include:

  • A comprehensive review on convolutional neural network in machine fault diagnosis, 2020, Neurocomputing
  • A review on the application of blind deconvolution in machinery fault diagnosis, 2021, Mechanical Systems and Signal Processing
  • Residual joint adaptation adversarial network for intelligent transfer fault diagnosis, 2020, Mechanical Systems and Signal Processing
  • Research on sparsity indexes for fault diagnosis of rotating machinery, 2020, Measurement
  • Double-level adversarial domain adaptation network for intelligent fault diagnosis, 2020, Knowledge-Based Systems

Ming Zhao frequently publishes in certain journals, indicating research impact and relevance within those venues. These include:

  • Mechanical Systems and Signal Processing
  • Measurement
  • Measurement Science and Technology
  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Microwave and Wireless Technology Letters

Collaborations are evident in Zhao's research, with recurrent coauthors including:

  • Jing Lin
  • Jinyang Jiao
  • Kaixuan Liang
  • Chuancang Ding
  • Zhipeng Ma

Best Publications

  • A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox

    Luyang Jing;Ming Zhao;Pin Li;Xiaoqiang Xu

  • A comprehensive review on convolutional neural network in machine fault diagnosis

    Jinyang Jiao;Ming Zhao;Jing Lin;Kaixuan Liang

  • An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    Luyang Jing;Taiyong Wang;Ming Zhao;Peng Wang

  • Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

    Yonghao Miao;Ming Zhao;Ming Zhao;Jing Lin;Yaguo Lei

  • A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

    Ming Zhao;Ming Zhao;Xiaodong Jia

  • Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification

    Yonghao Miao;Ming Zhao;Ming Zhao;Jing Lin

  • Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition.

    Yonghao Miao;Ming Zhao;Jing Lin

  • A review on the application of blind deconvolution in machinery fault diagnosis

    Yonghao Miao;Yonghao Miao;Boyao Zhang;Jing Lin;Ming Zhao

  • Residual joint adaptation adversarial network for intelligent transfer fault diagnosis

    Jinyang Jiao;Ming Zhao;Jing Lin;Kaixuan Liang

  • Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis

    Xiaoqiang Xu;Ming Zhao;Jing Lin;Yaguo Lei

  • A tacho-less order tracking technique for large speed variations

    Ming Zhao;Jing Lin;Xiufeng Wang;Yaguo Lei

  • Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds.

    Ming-Ming Zhao;Jing Lin;Xiaoqiang Xu;Yaguo Lei

  • Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis

    Jinyang Jiao;Ming Zhao;Jing Lin;Chuancang Ding

  • Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings

    Ming Zhao;Ming Zhao;Jing Lin;Yonghao Miao;Xiaoqiang Xu

  • Unsupervised Adversarial Adaptation Network for Intelligent Fault Diagnosis

    Jinyang Jiao;Ming Zhao;Jing Lin

  • Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis

    Dan He;Xiufeng Wang;Shancang Li;Jing Lin

  • Health Assessment of Rotating Machinery Using a Rotary Encoder

    Ming Zhao;Jing Lin

  • Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy

    Xiaodong Jia;Ming Zhao;Yuan Di;Qibo Yang

  • A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes

    Jinyang Jiao;Ming Zhao;Jing Lin;Jian Zhao

  • Double-level adversarial domain adaptation network for intelligent fault diagnosis

    Jinyang Jiao;Jing Lin;Ming Zhao;Kaixuan Liang

  • Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery

    Xiaodong Jia;Ming Zhao;Ming Zhao;Yuan Di;Pin Li

Frequent Co-Authors

Jing Lin
Jing Lin Beihang University
Yaguo Lei
Yaguo Lei Xi'an Jiaotong University
Jay Lee
Jay Lee University of Maryland, College Park
Viliam Makis
Viliam Makis University of Toronto
Shancang Li
Shancang Li University of the West of England
Liping Huang
Liping Huang Dalian University of Technology
Junyi Cao
Junyi Cao Xi'an Jiaotong University

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