World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
42
Citations
15798
World Ranking
1776
National Ranking
217

Naipeng Li publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Naipeng Li sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 103 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Naipeng Li D-index placement in Mechanical and Aerospace Engineering in 2026

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

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 42 D-Index — 49th percentile

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

The last bar groups every scientist with 93 D-Index or more.

Best Publications

  • Applications of machine learning to machine fault diagnosis: A review and roadmap

    Yaguo Lei;Bin Yang;Xinwei Jiang;Feng Jia

  • Machinery health prognostics: A systematic review from data acquisition to RUL prediction

    Yaguo Lei;Naipeng Li;Liang Guo;Ningbo Li

  • A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

    Biao Wang;Yaguo Lei;Naipeng Li;Ningbo Li

  • A recurrent neural network based health indicator for remaining useful life prediction of bearings

    Liang Guo;Naipeng Li;Feng Jia;Yaguo Lei

  • A Model-Based Method for Remaining Useful Life Prediction of Machinery

    Yaguo Lei;Naipeng Li;Szymon Gontarz;Jing Lin

  • Deep separable convolutional network for remaining useful life prediction of machinery

    Biao Wang;Yaguo Lei;Naipeng Li;Tao Yan

  • Applications of stochastic resonance to machinery fault detection: A review and tutorial

    Zijian Qiao;Yaguo Lei;Yaguo Lei;Naipeng Li

  • Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

    Biao Wang;Yaguo Lei;Tao Yan;Naipeng Li

  • XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial

    Unknown

  • Machinery health indicator construction based on convolutional neural networks considering trend burr

    Liang Guo;Yaguo Lei;Naipeng Li;Tao Yan

  • A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

    Bin Yang;Yaguo Lei;Feng Jia;Naipeng Li

  • Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings

    Zhijian Wang;Xinxin He;Bin Yang;Naipeng Li

  • Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery

    Biao Wang;Yaguo Lei;Naipeng Li;Wenting Wang

  • Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network

    Unknown

  • Intelligent Machinery Fault Diagnosis With Event-Based Camera

    Unknown

  • Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

    Unknown

  • A new fault diagnosis method based on adaptive spectrum mode extraction

    Unknown

  • Remaining useful life prediction based on a multi-sensor data fusion model

    Naipeng Li;Naipeng Li;Nagi Gebraeel;Yaguo Lei;Xiaolei Fang

  • A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults

    Saibo Xing;Yaguo Lei;Shuhui Wang;Na Lu

  • A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds

    Naipeng Li;Pengcheng Xu;Yaguo Lei;Xiao Cai

Frequent Co-Authors

Yaguo Lei
Yaguo Lei Xi'an Jiaotong University
Jing Lin
Jing Lin Beihang University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Zhengjia He
Zhengjia He Xi'an Jiaotong University
Asoke K. Nandi
Asoke K. Nandi Brunel University London

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