Kai Goebel
Zitiert von
Zitiert von
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
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
Prognostics in battery health management
K Goebel, B Saha, A Saxena, JR Celaya, JP Christophersen
IEEE instrumentation & measurement magazine 11 (4), 33-40, 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
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
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
Modeling Li-ion battery capacity depletion in a particle filtering framework
B Saha, K Goebel
Annual Conference of the PHM Society 1 (1), 2009
Turbofan engine degradation simulation data set
A Saxena, K Goebel
NASA ames prognostics data repository 18, 878-887, 2008
A Survey of Artificial Intelligence for Prognostics.
M Schwabacher, K Goebel
AAAI fall symposium: artificial intelligence for prognostics, 108-115, 2007
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
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
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
Fusing physics-based and deep learning models for prognostics
MA Chao, C Kulkarni, K Goebel, O Fink
Reliability Engineering & System Safety 217, 107961, 2022
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
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
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
Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques
B Saha, K Goebel
2008 IEEE aerospace conference, 1-8, 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
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
Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics
M Orchard, G Kacprzynski, K Goebel, B Saha, G Vachtsevanos
Applications of Intelligent Control to Engineering Systems, 23-35, 2009
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