World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
65
Citations
17593
World Ranking
1506
National Ranking
493

Kai Goebel 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 Kai Goebel 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: 398 publications — 89th percentile

89% of scientists in this discipline score the same or lower.

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

Kai Goebel 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 Kai Goebel 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: 65 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Overview

Kai Goebel is affiliated with the Palo Alto Research Center in the United States and has a research focus primarily within the field of Engineering. Their work encompasses several subfields, including Control and Systems Engineering, Statistics, Probability and Uncertainty, Safety, Risk, Reliability and Quality, Artificial Intelligence, and Mechanical Engineering.

The scope of Goebel's research topics includes Fault Detection and Control Systems, Machine Fault Diagnosis Techniques, Reliability and Maintenance Optimization, Risk and Safety Analysis, Advanced Battery Technologies Research, Probabilistic and Robust Engineering Design, and Software Reliability and Analysis Research.

Goebel has contributed to a range of academic venues, frequently publishing in the following journals and conferences:

  • International Journal of Prognostics and Health Management
  • Mechanical Systems and Signal Processing
  • Annual Conference of the PHM Society
  • arXiv (Cornell University)
  • Reliability Engineering & System Safety

Recent publications include:

  • Metrics for Offline Evaluation of Prognostic Performance, 2021, International Journal of Prognostics and Health Management
  • Fusing physics-based and deep learning models for prognostics, 2022, Repository for Publications and Research Data (ETH Zurich)
  • Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics, 2021, Data
  • Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks, 2021, Mechanical Systems and Signal Processing
  • Relation between prognostics predictor evaluation metrics and local interpretability SHAP values, 2022, Artificial Intelligence

Goebel's collaboration network includes frequent co-authors such as Chetan S. Kulkarni, Manuel Arias Chao, Olga Fink, Márcia L. Baptista, and Antonios Kamariotis.

Best Publications

  • Damage propagation modeling for aircraft engine run-to-failure simulation

    A. Saxena;K. Goebel;D. Simon;N. Eklund

  • Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

    B. Saha;K. Goebel;S. Poll;J. Christophersen

  • Prognostics in Battery Health Management

    K. Goebel;B. Saha;A. Saxena;J. Celaya

  • Metrics for evaluating performance of prognostic techniques

    A. Saxena;J. Celaya;E. Balaban;K. Goebel

  • Metrics for Offline Evaluation of Prognostic Performance

    Abhinav Saxena;Jose Celaya;Bhaskar Saha;Sankalita Saha

  • Comparison of prognostic algorithms for estimating remaining useful life of batteries

    Bhaskar Saha;Kai Goebel;Jon Christophersen

  • Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework

    Bhaskar Saha;Kai Goebel

  • Fusing physics-based and deep learning models for prognostics

    Manuel Arias Chao;Chetan S. Kulkarni;Kai Goebel;Olga Fink

  • A Survey of Artificial Intelligence for Prognostics.

    Mark Schwabacher;Kai Goebel

  • Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics

    N. Patil;J. Celaya;D. Das;K. Goebel

  • An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

    Jie Liu;Abhinav Saxena;Kai Goebel;Bhaskar Saha

  • Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics

    Manuel Arias Chao;Chetan S. Kulkarni;Kai Goebel;Olga Fink

  • Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

    Junchuan Shi;Dikang Peng;Zhongxiao Peng;Ziyang Zhang

  • Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

    E. Balaban;A. Saxena;P. Bansal;K.F. Goebel

  • An integrated approach to battery health monitoring using bayesian regression and state estimation

    B. Saha;K. Goebel;S. Poll;J. Christophersen

  • Hybrid soft computing systems: industrial and commercial applications

    P.P. Bonissone;Yu-To Chen;K. Goebel;P.S. Khedkar

  • Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

    B. Saha;K. Goebel

  • On Applying the Prognostic Performance Metrics

    Abhinav Saxena;Jose Celaya;Bhaskar Saha;Sankalita Saha

  • A diagnostic approach for electro-mechanical actuators in aerospace systems

    Edward Balaban;Prasun Bansal;Paul Stoelting;Abhinav Saxena

  • Model-Based Prognostics With Concurrent Damage Progression Processes

    M. J. Daigle;K. Goebel

  • SYSTEM AND METHOD FOR ISSUING CAUTION BY EVALUATING MULTIVARIABLE DATA

    Goebel Kai Frank;Doel David Lacey

Frequent Co-Authors

Abhinav Saxena
Abhinav Saxena General Electric (United States)
Pradeep Lall
Pradeep Lall Auburn University
Yongming Liu
Yongming Liu Arizona State University
George Vachtsevanos
George Vachtsevanos Georgia Institute of Technology
Jeffrey C. Suhling
Jeffrey C. Suhling Auburn University
Gautam Biswas
Gautam Biswas Vanderbilt University
Piero P. Bonissone
Piero P. Bonissone General Electric (United States)
Alice M. Agogino
Alice M. Agogino University of California, Berkeley
Marcos E. Orchard
Marcos E. Orchard University of Chile
Mark J. Balas
Mark J. Balas Texas A&M University

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