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D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
76
Citations
33163
World Ranking
263
National Ranking
34

Yaguo Lei 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 Yaguo Lei 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: 168 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

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

Yaguo Lei 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 Yaguo Lei 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: 76 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Overview

Yaguo Lei is affiliated with Xi'an Jiaotong University in China and focuses on research within the field of engineering, specifically emphasizing control and systems engineering, mechanical engineering, mechanics of materials, civil and structural engineering, and safety, risk, reliability, and quality.

Their research primarily covers the following main topics:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Reliability and Maintenance Optimization
  • Non-Destructive Testing Techniques
  • Engineering Diagnostics and Reliability
  • Tribology and Lubrication Engineering

Yaguo Lei frequently publishes in a number of specialized venues, with notable contributions to:

  • Mechanical Systems and Signal Processing
  • Reliability Engineering & System Safety
  • IEEE Transactions on Industrial Electronics
  • IEEE/CAA Journal of Automatica Sinica
  • SSRN Electronic Journal

Among recent papers authored or co-authored by Yaguo Lei are:

  • Applications of machine learning to machine fault diagnosis: A review and roadmap (2020), Mechanical Systems and Signal Processing
  • Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery (2020), IEEE Transactions on Industrial Electronics
  • Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution (2021), Mechanical Systems and Signal Processing
  • Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions (2020), IEEE Transactions on Industrial Electronics
  • Intelligent Machinery Fault Diagnosis With Event-Based Camera (2023), IEEE Transactions on Industrial Informatics

Collaborative research with frequent co-authors includes contributions with:

  • Naipeng Li
  • Xiang Li
  • Bin Yang
  • Junyi Cao
  • Wei-Hsin Liao

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 review on empirical mode decomposition in fault diagnosis of rotating machinery

    Yaguo Lei;Jing Lin;Zhengjia He;Ming J. Zuo

  • Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Feng Jia;Yaguo Lei;Jing Lin;Xin Zhou

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

    Biao Wang;Yaguo Lei;Naipeng Li;Ningbo Li

  • An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

    Yaguo Lei;Feng Jia;Jing Lin;Saibo Xing

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

    Liang Guo;Naipeng Li;Feng Jia;Yaguo Lei

  • An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

    Bin Yang;Yaguo Lei;Feng Jia;Saibo Xing

  • Condition monitoring and fault diagnosis of planetary gearboxes: A review

    Yaguo Lei;Jing Lin;Ming J. Zuo;Ming J. Zuo;Zhengjia He

  • Application of the EEMD method to rotor fault diagnosis of rotating machinery

    Yaguo Lei;Zhengjia He;Yanyang Zi

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

    Yaguo Lei;Naipeng Li;Szymon Gontarz;Jing Lin

  • Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs

    Yaguo Lei;Zhengjia He;Yanyang Zi;Qiao Hu

  • Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization

    Feng Jia;Yaguo Lei;Na Lu;Saibo Xing

  • Application of an improved kurtogram method for fault diagnosis of rolling element bearings

    Yaguo Lei;Jing Lin;Zhengjia He;Yanyang Zi

  • A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

    Feng Jia;Yaguo Lei;Liang Guo;Jing Lin

  • A new approach to intelligent fault diagnosis of rotating machinery

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • 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

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

    Biao Wang;Yaguo Lei;Naipeng Li;Tao Yan

  • EEMD method and WNN for fault diagnosis of locomotive roller bearings

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • Application of an intelligent classification method to mechanical fault diagnosis

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

    Yaguo Lei;Yaguo Lei;Dong Han;Jing Lin;Zhengjia He

  • Gear crack level identification based on weighted K nearest neighbor classification algorithm

    Yaguo Lei;Ming J. Zuo

  • New clustering algorithm-based fault diagnosis using compensation distance evaluation technique

    Yaguo Lei;Zhengjia He;Yanyang Zi;Xuefeng Chen

  • A multidimensional hybrid intelligent method for gear fault diagnosis

    Yaguo Lei;Ming J. Zuo;Zhengjia He;Yanyang Zi

Frequent Co-Authors

Jing Lin
Jing Lin Beihang University
Naipeng Li
Naipeng Li Xi'an Jiaotong University
Zhengjia He
Zhengjia He Xi'an Jiaotong University
Yanyang Zi
Yanyang Zi Xi'an Jiaotong University
Ming J. Zuo
Ming J. Zuo University of Alberta
Ming Zhao
Ming Zhao Xi'an Jiaotong University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Junyi Cao
Junyi Cao Xi'an Jiaotong University
Qinghua Hu
Qinghua Hu Tianjin University
Linkan Bian
Linkan Bian Mississippi State University

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