World's Best Scientists 2026 revealed!
Zhongkui Zhu

Zhongkui Zhu

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
6102
World Ranking
5937
National Ranking
1136

Zhongkui Zhu 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 Zhongkui Zhu 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: 184 publications — 41st percentile

41% of scientists in this discipline score the same or lower.

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

Zhongkui Zhu 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 Zhongkui Zhu 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: 44 D-Index — 42nd percentile

42% of scientists in this discipline score the same or lower.

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

Overview

Zhongkui Zhu is affiliated with Soochow University in China and has a prominent research profile in engineering. Their work is focused primarily on areas including control and systems engineering, mechanical engineering, mechanics of materials, artificial intelligence, and electrical and electronic engineering.

The research topics central to Zhu's publications encompass machine fault diagnosis techniques, gear and bearing dynamics analysis, fault detection and control systems, engineering diagnostics and reliability, structural health monitoring techniques, railway engineering and dynamics, and anomaly detection techniques and applications.

Zhu has contributed extensively to several academic journals and conferences. The most frequent venues for their publications include:

  • IEEE Transactions on Instrumentation and Measurement
  • Measurement Science and Technology
  • IEEE Sensors Journal
  • Mechanical Systems and Signal Processing
  • Advanced Engineering Informatics

Significant recent papers authored by Zhu cover a range of diagnostic technologies and methodologies for machinery, showing a strong focus on fault diagnosis and advanced signal processing methods. These papers include:

  • Multi-scale deep intra-class transfer learning for bearing fault diagnosis, 2020, published in Reliability Engineering & System Safety
  • Bearing fault diagnosis via generalized logarithm sparse regularization, 2021, published in Mechanical Systems and Signal Processing
  • Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions, 2020, published in Mechanical Systems and Signal Processing
  • Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines, 2022, published in Mechanism and Machine Theory
  • An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis, 2020, published in Structural Health Monitoring

Zhu frequently collaborates with several co-authors, indicating ongoing partnerships within the research community. These frequent collaborators include:

  • Weiguo Huang
  • Changqing Shen
  • Juanjuan Shi
  • Xingxing Jiang
  • Chuancang Ding

Best Publications

  • Multi-scale deep intra-class transfer learning for bearing fault diagnosis

    Xu Wang;Changqing Shen;Min Xia;Dong Wang

  • Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

    Yumei Qi;Changqing Shen;Dong Wang;Juanjuan Shi

  • Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis

    Shibin Wang;Weiguo Huang;Z.K. Zhu

  • Bearing fault diagnosis via generalized logarithm sparse regularization

    Ziwei Zhang;Weiguo Huang;Yi Liao;Zeshu Song

  • A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines

    Xingxing Jiang;Jun Wang;Juanjuan Shi;Changqing Shen

  • Fault diagnosis of rotating machines based on the EMD manifold

    Jun Wang;Guifu Du;Zhongkui Zhu;Changqing Shen

  • Initial center frequency-guided VMD for fault diagnosis of rotating machines

    Xingxing Jiang;Changqing Shen;Juanjuan Shi;Zhongkui Zhu

  • Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines

    Unknown

  • An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder

    Changqing Shen;Yumei Qi;Jun Wang;Gaigai Cai

  • Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions

    Qi Li;Changqing Shen;Liang Chen;Zhongkui Zhu

  • Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis

    Weiguo Huang;Guanqi Gao;Ning Li;Xingxing Jiang

  • Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection

    Haiyang Liu;Weiguo Huang;Weiguo Huang;Shibin Wang;Zhongkui Zhu;Zhongkui Zhu

  • Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis

    Unknown

  • An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis

    Xingxing Jiang;Jun Wang;Changqing Shen;Juanjuan Shi

  • Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis

    Ning Li;Weiguo Huang;Wenjun Guo;Guanqi Gao

  • Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction

    Wei Fan;Gaigai Cai;Gaigai Cai;Z.K. Zhu;Z.K. Zhu;Changqing Shen

  • Cyclostationarity analysis for gearbox condition monitoring: Approaches and effectiveness

    Z.K. Zhu;Z.H. Feng;F.R. Kong

  • Detection of signal transients based on wavelet and statistics for machine fault diagnosis

    Z.K. Zhu;Ruqiang Yan;Liheng Luo;Z.H. Feng

  • Nonlocal theoretical approaches and atomistic simulations for longitudinal free vibration of nanorods/nanotubes and verification of different nonlocal models

    Cheng Li;Shuang Li;Linquan Yao;Zhongkui Zhu

  • Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

    Shenghao Tang;Changqing Shen;Dong Wang;Shuang Li

  • Smart multichannel mode extraction for enhanced bearing fault diagnosis

    Unknown

  • Self-Adaptive Multivariate Variational Mode Decomposition and Its Application for Bearing Fault Diagnosis

    Unknown

  • A Wavelet-Based Statistical Approach for Monitoring and Diagnosis of Compound Faults With Application to Rolling Bearings

    Wei Fan;Qiang Zhou;Jian Li;Zhongkui Zhu

  • Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

    Shibin Wang;Shibin Wang;Gaigai Cai;Zhongkui Zhu;Zhongkui Zhu;Weiguo Huang

  • Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis

    Gaigai Cai;Gaigai Cai;Ivan W. Selesnick;Shibin Wang;Shibin Wang;Weiwei Dai

  • Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis

    Weiguo Huang;Ning Li;Ivan Selesnick;Juanjuan Shi

Frequent Co-Authors

Changqing Shen
Changqing Shen Soochow University
Xingxing Jiang
Xingxing Jiang Soochow University
Dong Wang
Dong Wang Shanghai Jiao Tong University
Wei You
Wei You University of North Carolina at Chapel Hill
Cristian Garcia
Cristian Garcia Andrés Bello University
Jose Rodriguez
Jose Rodriguez San Sebastián University
Ivan W. Selesnick
Ivan W. Selesnick New York University
Fanrang Kong
Fanrang Kong University of Science and Technology of China
Yihua Hu
Yihua Hu University of York
Qingbo He
Qingbo He Shanghai Jiao Tong University

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