World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
8721
World Ranking
7173
National Ranking
949

Hongkai Jiang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Hongkai Jiang sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 96 publications — 7th percentile

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

The last bar groups every scientist with 991 publications or more.

Hongkai Jiang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Hongkai Jiang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Hongkai Jiang is affiliated with Northwestern Polytechnical University in China and has an extensive research portfolio primarily within the field of Engineering. Their work spans several subfields, with significant contributions in Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials, Artificial Intelligence, and Computer Vision and Pattern Recognition.

The core themes of Jiang's research focus on machine fault diagnosis techniques, gear and bearing dynamics analysis, fault detection and control systems, and engineering diagnostics and reliability. Additional topics include advanced machining processes and optimization, imbalanced data classification techniques, and anomaly detection techniques and applications.

Frequent publication venues where Jiang's work appears include:

  • Measurement Science and Technology
  • Mechanical Systems and Signal Processing
  • Advanced Engineering Informatics
  • Knowledge-Based Systems
  • ISA Transactions

Jiang collaborates regularly with a group of coauthors who have contributed to multiple projects, including Yutong Dong, Zhenghong Wu, Xin Wang, Renhe Yao, and Yunpeng Liu.

Recent published papers illustrate the research scope and focus. Titles, years, and venues include:

  • Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis, 2021, Mechanical Systems and Signal Processing
  • Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis, 2021, Knowledge-Based Systems
  • An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm, 2020, Mechanical Systems and Signal Processing
  • Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine, 2021, IEEE Transactions on Industrial Electronics
  • Rolling bearing fault diagnosis using optimal ensemble deep transfer network, 2020, Knowledge-Based 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

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

    Haidong Shao;Hongkai Jiang;Haizhou Zhang;Wenjing Duan

  • An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis

    Hongkai Jiang;Chengliang Li;Huaxing Li

  • An adaptive deep transfer learning method for bearing fault diagnosis

    Zhenghong Wu;Hongkai Jiang;Ke Zhao;Xingqiu Li

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

    Haidong Shao;Hongkai Jiang;Fuan Wang;Huiwei Zhao

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

    Haidong Shao;Hongkai Jiang;Fuan Wang;Yanan Wang

  • An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm

    Xingqiu Li;Hongkai Jiang;Maogui Niu;Ruixin Wang

  • Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis

    Shaowei Liu;Hongkai Jiang;Zhenghong Wu;Xingqiu Li

  • Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis

    Ke Zhao;Hongkai Jiang;Kaibo Wang;Zeyu Pei

  • 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

  • Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis

    Shaowei Liu;Hongkai Jiang;Zhenghong Wu;Xingqiu Li

  • Rolling bearing fault diagnosis using optimal ensemble deep transfer network

    Xingqiu Li;Hongkai Jiang;Ruixin Wang;Maogui Niu

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

    Xingqiu Li;Hongkai Jiang;Xiong Xiong;Haidong Shao

  • A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis

    Unknown

  • A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data

    Zhenghong Wu;Hongkai Jiang;Tengfei Lu;Ke Zhao

  • A reinforcement neural architecture search method for rolling bearing fault diagnosis

    Ruixin Wang;Hongkai Jiang;Xingqiu Li;Shaowei Liu

Frequent Co-Authors

Haidong Shao
Haidong Shao Hunan University
Junsheng Cheng
Junsheng Cheng Hunan University

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