World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
45
Citations
8187
World Ranking
1524
National Ranking
184

Baoping Tang 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 Baoping Tang 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: 162 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Baoping Tang 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 Baoping Tang 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: 45 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Best Publications

  • Deep Residual Shrinkage Networks for Fault Diagnosis

    Minghang Zhao;Shisheng Zhong;Xuyun Fu;Baoping Tang

  • Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

    Minghang Zhao;Myeongsu Kang;Baoping Tang;Michael Pecht

  • The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

    Unknown

  • Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution

    Unknown

  • Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units

    Unknown

  • A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

    Unknown

  • Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

    Unknown

  • Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

    Minghang Zhao;Shisheng Zhong;Xuyun Fu;Baoping Tang

  • Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks

    Unknown

  • Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

    Minghang Zhao;Myeongsu Kang;Baoping Tang;Michael Pecht

  • Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD

    Unknown

  • Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis

    Unknown

  • Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine

    Unknown

  • A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels

    Unknown

  • Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

    Unknown

  • Status and problems of wind turbine structural health monitoring techniques in China

    Unknown

  • An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes

    Unknown

  • Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction

    Unknown

  • Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings

    Unknown

  • Order spectrogram visualization for rolling bearing fault detection under speed variation conditions

    Yi Wang;Yi Wang;Peter W. Tse;Baoping Tang;Yi Qin

Frequent Co-Authors

David He
David He University of Illinois at Chicago

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