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Engineering and Technology

D-Index
65
Citations
17593
World Ranking
1504
National Ranking
492

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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