World's Best Scientists 2026 revealed!
Haidong Shao

Haidong Shao

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
50
Citations
10329
World Ranking
320
National Ranking
107

Engineering and Technology

D-Index
53
Citations
12535
World Ranking
3352
National Ranking
683

Haidong Shao 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 Haidong Shao 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: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Haidong Shao 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 Haidong Shao 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: 53 D-Index — 66th percentile

66% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Haidong Shao is a researcher affiliated with Hunan University in China, specializing in engineering with a focus on control and systems engineering, mechanical engineering, artificial intelligence, mechanics of materials, and industrial and manufacturing engineering. Their work predominantly engages with machine fault diagnosis and related reliability and control topics.

Their research covers key topics such as:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Anomaly Detection Techniques and Applications
  • Engineering Diagnostics and Reliability
  • Industrial Vision Systems and Defect Detection
  • Machine Learning in Bioinformatics

Haidong Shao has contributed to multiple scholarly articles, including numerous papers published in influential venues. Frequent publication platforms include:

  • Reliability Engineering & System Safety
  • Measurement Science and Technology
  • Advanced Engineering Informatics
  • Knowledge-Based Systems
  • IEEE Transactions on Industrial Informatics

Among recent significant papers authored or co-authored by Haidong Shao are:

  • "Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images" (2020), published in IEEE Transactions on Industrial Informatics
  • "A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance" (2021), published in Information Fusion

In their collaborations, Shao frequently works with peers who have contributed extensively to the field, including Bin Liu, Yiming Xiao, Junsheng Cheng, Shen Yan, and Yu Yang.

Their scholarly activity largely centers on the intersection of engineering disciplines and artificial intelligence to advance diagnostic methodologies for machinery and fault detection. This includes research on thermal imaging, deep transfer learning, and multisensory fusion techniques applied to maintenance and reliability systems.

Best Publications

  • A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

    Haidong Shao;Hongkai Jiang;Huiwei Zhao;Fuan Wang

  • Rolling bearing fault diagnosis using an optimization deep belief network

    Haidong Shao;Hongkai Jiang;Xun Zhang;Maogui Niu

  • Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network

    Haidong Shao;Hongkai Jiang;Haizhou Zhang;Tianchen Liang

  • A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

    Haidong Shao;Hongkai Jiang;Ying Lin;Xingqiu Li

  • Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

    Haidong Shao;Min Xia;Guangjie Han;Yu Zhang

  • Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

    Unknown

  • Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

    Haidong Shao;Hongkai Jiang;Haizhou Zhang;Wenjing Duan

  • A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

    Min Xia;Haidong Shao;Xiandong Ma;Clarence W. de Silva

  • Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

    Min Xia;Haidong Shao;Darren Williams;Siliang Lu

  • An enhancement deep feature fusion method for rotating machinery fault diagnosis

    Haidong Shao;Hongkai Jiang;Fuan Wang;Huiwei Zhao

  • LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention

    Unknown

  • Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.

    Haidong Shao;Hongkai Jiang;Fuan Wang;Yanan Wang

  • A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

    Haidong Shao;Haidong Shao;Jing Lin;Liangwei Zhang;Diego Galar

  • Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

    Hongru Cao;Haidong Shao;Haidong Shao;Xiang Zhong;Qianwang Deng

  • Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing

    Unknown

  • Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network

    Unknown

  • Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder

    He Zhiyi;Shao Haidong;Shao Haidong;Jing Lin;Cheng Junsheng

  • An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    Unknown

  • Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

    Zhiyi He;Haidong Shao;Xiang Zhong;Xianzhu Zhao

  • 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

  • Modified Stacked Auto-encoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

    Haidong Shao;Min Xia;Jiafu Wan;Clarence De Silva

  • Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

    Hongkai Jiang;Xingqiu Li;Haidong Shao;Ke Zhao

  • Rolling bearing fault detection using continuous deep belief network with locally linear embedding

    Haidong Shao;Hongkai Jiang;Xingqiu Li;Tianchen Liang

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

    Zhiyi He;Haidong Shao;Ziyang Ding;Hongkai Jiang

  • Improved Deep Transfer Auto-Encoder for Fault Diagnosis of Gearbox Under Variable Working Conditions With Small Training Samples

    Zhiyi He;Haidong Shao;Xiaoyang Zhang;Junsheng Cheng

  • Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network

    Xingqiu Li;Hongkai Jiang;Xiong Xiong;Haidong Shao

  • A Fusion CWSMM-based Framework for Rotating Machinery Fault Diagnosis under Strong Interference and Imbalanced Case

    Xin Li;Jian Cheng;Haidong Shao;Kan Liu

Frequent Co-Authors

Hongkai Jiang
Hongkai Jiang Northwestern Polytechnical University
Junsheng Cheng
Junsheng Cheng Hunan University
Clarence W. de Silva
Clarence W. de Silva University of British Columbia
Jing Lin
Jing Lin Shenzhen University
Jiafu Wan
Jiafu Wan South China University of Technology
Changqing Shen
Changqing Shen Soochow University
Siliang Lu
Siliang Lu Anhui University
Baoping Cai
Baoping Cai China University of Petroleum, Beijing

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