Kai Goebel

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Abhinav SaxenaGE Aerospace ResearchVerified email at ge.com
Jose CelayaSchlumberger, Software Technology and Innovation CenterVerified email at slb.com
Bhaskar SahaPalo Alto Research CenterVerified email at parc.com
Chetan S. KulkarniSenior Scientist at NASA Ames Research Center (KBR, Inc)Verified email at nasa.gov
Matthew J. DaiglePrincipal Scientist, NovityVerified email at novity.us
Pradeep LallAuburn UniversityVerified email at auburn.edu
Shankar SankararamanData Scientist and Machine Learning, IntuitVerified email at intuit.com
Weizhong Yan, PhD, Fellow PHMSGE Global Research CenterVerified email at ge.com
Indranil RoychoudhuryAI Scientist, Software Technology Innovation Center, SchlumbergerVerified email at slb.com
Piero P. BonissoneIndependent Consultant, Former Chief Scientist at GE Global ResearchVerified email at ieee.org
Alice M. AGOGINOProfessor of Mechancial Engineering, University of California at BerkeleyVerified email at berkeley.edu
Gautam BiswasCornelius Vanderbilt Professor of Engineering; Professor of Computer Science and EngineeringVerified email at vanderbilt.edu
Yongming LiuArizona State UniversityVerified email at asu.edu
Ryan LoweAuburn UniversityVerified email at auburn.edu
Marcos OrchardUniversity of ChileVerified email at ing.uchile.cl
Jeffrey C. SuhlingAuburn University, Department of Mechanical EngineeringVerified email at auburn.edu
JUAN CHIACHIO RUANOUniversity of GranadaVerified email at ugr.es
Irem Y TumerProfessor of Mechanical Engineering, Oregon State UniversityVerified email at oregonstate.edu
Christopher HoyleAssociate Professor of Mechanical Engineering, Oregon State UniversityVerified email at oregonstate.edu
Brian BoleData Scientist, ARMUS Corp.Verified email at armus.com

Kai Goebel

Fragum Global

Verified email at fragumglobal.com - Homepage

Prognostics Health Management Resilient Design Decision-Making Hybrid Systems AI


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Damage propagation modeling for aircraft engine run-to-failure simulation A Saxena, K Goebel, D Simon, N Eklund 2008 international conference on prognostics and health management, 1-9, 2008	1968	2008
Prognostics methods for battery health monitoring using a Bayesian framework B Saha, K Goebel, S Poll, J Christophersen IEEE Transactions on instrumentation and measurement 58 (2), 291-296, 2008	952	2008
Prognostics in battery health management K Goebel, B Saha, A Saxena, JR Celaya, JP Christophersen IEEE instrumentation & measurement magazine 11 (4), 33-40, 2008	700	2008
Metrics for evaluating performance of prognostic techniques A Saxena, J Celaya, E Balaban, K Goebel, B Saha, S Saha, ... 2008 international conference on prognostics and health management, 1-17, 2008	675	2008
Metrics for offline evaluation of prognostic performance A Saxena, J Celaya, B Saha, S Saha, K Goebel International Journal of Prognostics and Health Management 1 (1), 20, 2010	572	2010
Comparison of prognostic algorithms for estimating remaining useful life of batteries B Saha, K Goebel, J Christophersen Transactions of the Institute of Measurement and Control 31 (3-4), 293-308, 2009	530	2009
Turbofan engine degradation simulation data set A Saxena, K Goebel NASA ames prognostics data repository 18, 878-887, 2008	480	2008
Modeling Li-ion battery capacity depletion in a particle filtering framework B Saha, K Goebel Annual Conference of the PHM Society 1 (1), 2009	471	2009
A Survey of Artificial Intelligence for Prognostics. M Schwabacher, K Goebel AAAI fall symposium: artificial intelligence for prognostics, 108-115, 2007	396	2007
Fusing physics-based and deep learning models for prognostics MA Chao, C Kulkarni, K Goebel, O Fink Reliability Engineering & System Safety 217, 107961, 2022	331	2022
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries J Liu, A Saxena, K Goebel, B Saha, W Wang Annual Conference of the Prognostics and Health Management Society, 2010	317	2010
Precursor parameter identification for insulated gate bipolar transistor (IGBT) prognostics N Patil, J Celaya, D Das, K Goebel, M Pecht IEEE Transactions on Reliability 58 (2), 271-276, 2009	281	2009
An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation B Saha, S Poll, K Goebel, J Christophersen Proceedings of IEEE Autotestcon, 2007	265	2007
Modeling, detection, and disambiguation of sensor faults for aerospace applications E Balaban, A Saxena, P Bansal, KF Goebel, S Curran IEEE Sensors Journal 9 (12), 1907-1917, 2009	245	2009
A diagnostic approach for electro-mechanical actuators in aerospace systems E Balaban, P Bansal, P Stoelting, A Saxena, KF Goebel, S Curran Aerospace conference, 2009 IEEE, 1-13, 2009	239	2009
A comparison of three data-driven techniques for prognostics K Goebel, B Saha, A Saxena, N Mct, N Riacs 62nd meeting of the society for machinery failure prevention technology …, 2008	239	2008
Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques B Saha, K Goebel 2008 IEEE aerospace conference, 1-8, 2008	230	2008
On applying the prognostic performance metrics A Saxena, J Celaya, B Saha, S Saha, K Goebel Annual Conference of the PHM Society 1 (1), 2009	229	2009
Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics M Arias Chao, C Kulkarni, K Goebel, O Fink Data 6 (1), 5, 2021	220	2021
Hybrid soft computing systems: Industrial and commercial applications PP Bonissone, YT Chen, K Goebel, PS Khedkar Proceedings of the IEEE 87 (9), 1641-1667, 1999	215	1999

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