World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
49
Citations
10964
World Ranking
4252
National Ranking
844

Peter W. Tse 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 Peter W. Tse 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: 203 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Peter W. Tse 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 Peter W. Tse 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: 49 D-Index — 57th percentile

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

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

Overview

Peter W. Tse is a researcher affiliated with the City University of Hong Kong in China. Their primary field of study is Engineering, with significant focus on subfields such as Mechanics of Materials, Mechanical Engineering, Civil and Structural Engineering, Ocean Engineering, and Computer Vision and Pattern Recognition.

The main research topics covered by Peter W. Tse include:

  • Ultrasonics and Acoustic Wave Propagation
  • Non-Destructive Testing Techniques
  • Structural Health Monitoring Techniques
  • Geophysical Methods and Applications
  • Thermography and Photoacoustic Techniques
  • Fatigue and fracture mechanics
  • Railway Engineering and Dynamics

Peter W. Tse has contributed to several recent research papers, including:

  • "The use of ultrasonic guided waves for the inspection of square tube structures: Dispersion analysis and numerical and experimental studies," 2020, Structural Health Monitoring
  • "Non-contact detection of railhead defects and their classification by using convolutional neural network," 2022, Optik
  • "Estimation of remaining useful life of fatigued plate specimens using Lamb wave-based nonlinearity parameters," 2020, Structural Control and Health Monitoring
  • "Methodology for circumferential localisation of defects within small-diameter concrete-covered pipes based on changing of energy distribution of non-axisymmetric guided waves," 2020, Applied Acoustics
  • "Extraction of Least-Dispersive Ultrasonic Guided Wave Mode in Rail Track Based on Floquet-Bloch Theory," 2021, Shock and Vibration

Frequent collaborators with whom Peter W. Tse has co-authored multiple works include:

  • Faeez Masurkar
  • Nitesh P. Yelve
  • Javad Rostami
  • Imran Ghafoor
  • Zhou Fang

Publication venues that have frequently featured their research are:

  • Sensors
  • Structural Health Monitoring
  • Applied Acoustics
  • Measurement
  • IEEE Transactions on Instrumentation and Measurement

Best Publications

  • A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing

    Z.K. Peng;Peter W. Tse;F.L. Chu

  • An improved Hilbert Huang transform and its application in vibration signal analysis

    Z.K. Peng;Peter W. Tse;F.L. Chu

  • Intelligent Predictive Decision Support System for Condition-Based Maintenance

    R. C. M. Yam;P.W. Tse;L. Li;P. Tu

  • Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

    Peter W. Tse;Y. H. Peng;Richard Yam

  • An enhanced Kurtogram method for fault diagnosis of rolling element bearings

    Dong Wang;Peter W. Tse;Kwok Leung Tsui

  • Application of mother wavelet functions for automatic gear and bearing fault diagnosis

    J. Rafiee;M. A. Rafiee;P. W. Tse

  • Machine fault diagnosis through an effective exact wavelet analysis

    Peter W. Tse;Wen-xian Yang;H.Y. Tam

  • The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2”

    Peter W. Tse;Dong Wang

  • Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier

    Changqing Shen;Changqing Shen;Dong Wang;Fanrang Kong;Peter W. Tse

  • A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system

    J. Rafiee;P. W. Tse;A. Harifi;M. H. Sadeghi

  • Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks

    P. W. Tse;D. P. Atherton

  • A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals

    Wei Guo;Peter W. Tse

  • Use of autocorrelation of wavelet coefficients for fault diagnosis

    J. Rafiee;P.W. Tse

  • Development of an advanced noise reduction method for vibration analysis based on singular value decomposition

    Wen-Xian Yang;Wen-Xian Yang;Peter W. Tse

  • Detection of the rubbing-caused impacts for rotor–stator fault diagnosis using reassigned scalogram

    Z.K. Peng;F.L. Chu;Peter W. Tse

  • EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine

    Yujun Li;Peter W. Tse;Xin Yang;Jianguo Yang

  • Anomaly Detection Through a Bayesian Support Vector Machine

    Vasilis A Sotiris;Peter W Tse;Michael G Pecht

  • Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition

    Wei Guo;Peter W. Tse;Alexandar Djordjevich

  • Adaptive backstepping output feedback control for a class of nonlinear fractional order systems

    Yiheng Wei;Peter W. Tse;Zhao Yao;Yong Wang

  • A comprehensive reliability allocation method for design of CNC lathes

    Yiqiang Wang;Richard C. M. Yam;Ming Jian Zuo;Peter W. Tse

  • Classification of gear faults using cumulants and the radial basis function network

    Lai Wuxing;Peter W. Tse;Zhang Guicai;Shi Tielin

Frequent Co-Authors

Dong Wang
Dong Wang Peking University
Yong Wang
Yong Wang University of Science and Technology of China
Changqing Shen
Changqing Shen Soochow University
Kwok-Leung Tsui
Kwok-Leung Tsui Virginia Tech
Richard C.M. Yam
Richard C.M. Yam City University of Hong Kong
Michael Pecht
Michael Pecht University of Maryland, College Park
Fulei Chu
Fulei Chu Tsinghua University
Zhike Peng
Zhike Peng Shanghai Jiao Tong University
Fanrang Kong
Fanrang Kong University of Science and Technology of China
Enrico Zio
Enrico Zio Polytechnic University of Milan

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