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2020 – today
- 2024
[j42]Kai Goebel
, Shantanu Rane:
AI in Industrial IoT Cybersecurity [Industrial and Governmental Activities]. IEEE Comput. Intell. Mag. 19(1): 14-15 (2024)- [j41]Antonios Kamariotis, Konstantinos Tatsis, Eleni N. Chatzi, Kai Goebel, Daniel Straub:
A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance. Reliab. Eng. Syst. Saf. 242: 109723 (2024) - [j40]Kristupas Bajarunas, Marcia Lourenco Baptista, Kai Goebel, Manuel Arias Chao:
Health index estimation through integration of general knowledge with unsupervised learning. Reliab. Eng. Syst. Saf. 251: 110352 (2024) - [i5]Antonios Kamariotis, Eleni N. Chatzi, Daniel Straub, Nikolaos Dervilis, Kai Goebel, Aidan J. Hughes, Geert Lombaert, Costas Papadimitriou, Konstantinos G. Papakonstantinou, Matteo Pozzi, Michael Todd, Keith Worden:
Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems. CoRR abs/2402.00021 (2024) - [i4]Kristupas Bajarunas, Marcia Lourenco Baptista, Kai Goebel, Manuel Arias Chao:
Health Index Estimation Through Integration of General Knowledge with Unsupervised Learning. CoRR abs/2405.04990 (2024) - 2022
- [j39]Marcia Lourenco Baptista, Kai Goebel, Elsa M. P. Henriques:
Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artif. Intell. 306: 103667 (2022) - [j38]Manuel Arias Chao, Chetan S. Kulkarni, Kai Goebel, Olga Fink:
Fusing physics-based and deep learning models for prognostics. Reliab. Eng. Syst. Saf. 217: 107961 (2022) - 2021
- [j37]Madhav Mishra, Jesper Martinsson, Kai Goebel, Matti Rantatalo:
Bearing Life Prediction With Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach. IEEE Access 9: 157002-157011 (2021) - [j36]Manuel Arias Chao, Chetan S. Kulkarni, Kai Goebel, Olga Fink:
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics. Data 6(1): 5 (2021) - [j35]Marcia Lourenco Baptista, Elsa M. P. Henriques, Kai Goebel:
A self-organizing map and a normalizing multi-layer perceptron approach to baselining in prognostics under dynamic regimes. Neurocomputing 456: 268-287 (2021) - [j34]Daniel E. Hulse, Arpan Biswas, Christopher Hoyle, Irem Y. Tumer, Chetan S. Kulkarni, Kai Goebel:
Exploring Architectures for Integrated Resilience Optimization. J. Aerosp. Inf. Syst. 18(10): 665-678 (2021) - [j33]Junchuan Shi, Tianyu Yu, Kai Goebel, Dazhong Wu:
Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features. J. Comput. Inf. Sci. Eng. 21(2) (2021) - [j32]Gina Sierra, Elinirina Iréna Robinson, Kai Goebel:
Improving tail accuracy of the predicted cumulative distribution function of time of failure. Reliab. Eng. Syst. Saf. 207: 107333 (2021) - 2020
- [j31]Jueming Hu, Heinz Erzberger, Kai Goebel, Yongming Liu:
Probabilistic Risk-Based Operational Safety Bound for Rotary-Wing Unmanned Aircraft Systems Traffic Management. J. Aerosp. Inf. Syst. 17(3): 171-181 (2020) - [i3]Manuel Arias Chao, Chetan S. Kulkarni, Kai Goebel, Olga Fink:
Fusing Physics-based and Deep Learning Models for Prognostics. CoRR abs/2003.00732 (2020) - [i2]Yuan Tian, Manuel Arias Chao, Chetan S. Kulkarni, Kai Goebel, Olga Fink:
Real-Time Model Calibration with Deep Reinforcement Learning. CoRR abs/2006.04001 (2020)
2010 – 2019
- 2019
- [j30]Gina Sierra, Marcos E. Orchard, Kai Goebel, Chetan S. Kulkarni:
Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms. Reliab. Eng. Syst. Saf. 182: 166-178 (2019) - [i1]Manuel Arias Chao, Chetan S. Kulkarni, Kai Goebel, Olga Fink:
Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models. CoRR abs/1908.01529 (2019) - 2018
- [j29]Marco Rigamonti, Piero Baraldi, Enrico Zio, Indranil Roychoudhury, Kai Goebel, Scott Poll:
Ensemble of optimized echo state networks for remaining useful life prediction. Neurocomputing 281: 121-138 (2018) - [j28]Xiaoge Zhang, Sankaran Mahadevan, Shankar Sankararaman, Kai Goebel:
Resilience-based network design under uncertainty. Reliab. Eng. Syst. Saf. 169: 364-379 (2018) - [j27]Madhav Mishra, Jesper Martinsson, Matti Rantatalo, Kai Goebel:
Bayesian hierarchical model-based prognostics for lithium-ion batteries. Reliab. Eng. Syst. Saf. 172: 25-35 (2018) - 2017
- [j26]Manuel Chiachío, Juan Chiachío, Shankar Sankararaman, Kai Goebel, John D. Andrews:
A new algorithm for prognostics using Subset Simulation. Reliab. Eng. Syst. Saf. 168: 189-199 (2017) - 2015
- [j25]Chetan S. Kulkarni, George Gorospe, Matthew J. Daigle, Kai Goebel:
A testbed for implementing prognostic methodologies on cryogenic propellant loading systems. IEEE Instrum. Meas. Mag. 18(4): 5-15 (2015) - [j24]Edward Balaban, Abhinav Saxena, Sriram Narasimhan, Indranil Roychoudhury, Michael Koopmans, Carl Ott, Kai Goebel:
Prognostic Health-Management System Development for Electromechanical Actuators. J. Aerosp. Inf. Syst. 12(3): 329-344 (2015) - [j23]Juan Chiachío, Manuel Chiachío, Shankar Sankararaman, Abhinav Saxena, Kai Goebel:
Condition-based prediction of time-dependent reliability in composites. Reliab. Eng. Syst. Saf. 142: 134-147 (2015) - [j22]Bin Zhang, Liang Tang, Jonathan A. DeCastro, Michael J. Roemer, Kai Goebel:
A Recursive Receding Horizon Planning for Unmanned Vehicles. IEEE Trans. Ind. Electron. 62(5): 2912-2920 (2015) - 2014
- [j21]Shankar Sankararaman, Matthew J. Daigle, Kai Goebel:
Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods. IEEE Trans. Reliab. 63(2): 603-619 (2014) - 2013
- [j20]Matthew J. Daigle, Kai Goebel:
Model-Based Prognostics With Concurrent Damage Progression Processes. IEEE Trans. Syst. Man Cybern. Syst. 43(3): 535-546 (2013) - 2012
- [j19]Jonny Carlos da Silva, Abhinav Saxena, Edward Balaban, Kai Goebel:
A knowledge-based system approach for sensor fault modeling, detection and mitigation. Expert Syst. Appl. 39(12): 10977-10989 (2012) - [j18]Pradeep Lall, Ryan Lowe, Kai Goebel:
Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics. IEEE Trans. Ind. Electron. 59(11): 4301-4314 (2012) - [j17]Pradeep Lall, Ryan Lowe, Kai Goebel:
Extended Kalman Filter Models and Resistance Spectroscopy for Prognostication and Health Monitoring of Leadfree Electronics Under Vibration. IEEE Trans. Reliab. 61(4): 858-871 (2012) - [j16]Pradeep Lall, Prashant Gupta, Kai Goebel:
Decorrelated Feature Space and Neural Nets Based Framework for Failure Modes Clustering in Electronics Subjected to Mechanical Shock. IEEE Trans. Reliab. 61(4): 884-900 (2012) - [c15]Brian Bole, Kai Goebel, George J. Vachtsevanos:
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions. Infotech@Aerospace 2012 - [c14]Jose Celaya, Abhinav Saxena, Kai Goebel:
A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components. Infotech@Aerospace 2012 - [c13]Susan A. Frost, Kai Goebel, Jose Celaya:
A Briefing on Metrics and Risks for Autonomous Decision-making in Aerospace Applications. Infotech@Aerospace 2012 - [c12]Chetan S. Kulkarni, Jose Celaya, Gautam Biswas, Kai Goebel:
Prognostics Health Management and Physics Based Failure Models for Electrolytic Capacitors. Infotech@Aerospace 2012 - [c11]Abhinav Saxena, Indranil Roychoudhury, Jose Celaya, Bhaskar Saha, Sankalita Saha, Kai Goebel:
Requirements Flowdown for Prognostics and Health Management. Infotech@Aerospace 2012 - [c10]Sonia Vohnout, Byoung Kim, Neil Kunst, Bill Gleeson, Robert Wagoner, Edward Balaban, Kai Goebel:
A Model-based Avionic Prognostic Reasoner (MAPR). Infotech@Aerospace 2012 - 2011
- [j15]Pradeep Lall, Chandan Bhat, Madhura Hande, Vikrant More, Rahul Vaidya, Kai Goebel:
Prognostication of Residual Life and Latent Damage Assessment in Lead-Free Electronics Under Thermomechanical Loads. IEEE Trans. Ind. Electron. 58(7): 2605-2616 (2011)
2000 – 2009
- 2009
- [j14]Bhaskar Saha, Kai Goebel, Scott Poll, Jon Christophersen:
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework. IEEE Trans. Instrum. Meas. 58(2): 291-296 (2009) - [j13]Nishad Patil, Jose Celaya, Diganta Das, Kai Goebel, Michael G. Pecht:
Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics. IEEE Trans. Reliab. 58(2): 271-276 (2009) - 2008
- [j12]Jerry T. Ball, Chris Arney, Samuel G. Collins, Mitchell Marcus, Sergei Nirenburg, Antonio Chella, Kai Goebel, Jason H. Li, Margaret Lyell, Brian Magerko, Riccardo Manzotti, Clayton T. Morrison, Tim Oates, Mark O. Riedl, Goran Trajkovski, Walt Truszkowski, N. Serdar Uckun:
AAAI Fall Symposium Reports. AI Mag. 29(1): 99-104 (2008) - [j11]Kai Goebel, Weizhong Yan:
Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning. IEEE Syst. J. 2(2): 189-197 (2008) - 2007
- [j10]William Cheetham, Kai Goebel:
Appliance Call Center: A Successful Mixed-Initiative Case Study. AI Mag. 28(2): 89-100 (2007) - [c9]Asif Khalak, Kai Goebel:
Health-Management Driven Control Reconfiguration Approach for Flight Vehicles. AAAI Fall Symposium: Artificial Intelligence for Prognostics 2007: 58-62 - [c8]Bhaskar Saha, Kai Goebel, Scott Poll, Jon Christophersen:
A Bayesian Framework for Remaining Useful Life Estimation. AAAI Fall Symposium: Artificial Intelligence for Prognostics 2007: 97-102 - [c7]Mark Schwabacher, Kai Goebel:
A Survey of Artificial Intelligence for Prognostics. AAAI Fall Symposium: Artificial Intelligence for Prognostics 2007: 108-115 - [c6]Liang Tang, Gregory J. Kacprzynski, Kai Goebel, Johan Reimann, Marcos E. Orchard, Abhinav Saxena, Bhaskar Saha:
Prognostics in the Control Loop. AAAI Fall Symposium: Artificial Intelligence for Prognostics 2007: 129-136 - [c5]Kalyan Veeramachaneni, Weizhong Yan, Kai Goebel, Lisa Ann Osadciw:
Improving Classifier Fusion Using Particle Swarm Optimization. MCDM 2007: 128-135 - [e1]George J. Vachtsevanos, N. Serdar Uckun, Kai Goebel:
Artificial Intelligence for Prognostics, Papers from the 2007 AAAI Fall Symposium, Arlington, Virginia, USA, November 9-11, 2007. AAAI Technical Report FS-07-02, AAAI Press 2007 [contents] - 2006
- [c4]Neil H. W. Eklund, Kai F. Goebel:
Using Meta-Features to Boost the Performance of Classifier Fusion Schemes for Time Series Data. IJCNN 2006: 3223-3230 - 2005
- [j9]Ion Muslea, Virginia Dignum, Daniel D. Corkill, Catholijn M. Jonker, Frank Dignum, Silvia Coradeschi, Alessandro Saffiotti, Dan Fu, Jeff Orkin, William Cheetham, Kai Goebel, Piero P. Bonissone, Leen-Kiat Soh, Randolph M. Jones, Robert E. Wray III, Matthias Scheutz, Daniela Pucci de Farias, Shie Mannor, Georgios Theocharous, Doina Precup, Bamshad Mobasher, Sarabjot S. Anand, Bettina Berendt, Andreas Hotho, Hans W. Guesgen, Michael T. Rosenstein, Mohammad Ghavamzadeh:
The Workshop Program at the Nineteenth National Conference on Artificial Intelligence. AI Mag. 26(1): 103-108 (2005) - [j8]Raj Subbu, Kai Goebel, Dean K. Frederick:
Evolutionary design and optimization of aircraft engine controllers. IEEE Trans. Syst. Man Cybern. Part C 35(4): 554-565 (2005) - [c3]Piero P. Bonissone, Neil H. W. Eklund, Kai Goebel:
Using an Ensemble of Classifiers to Audit a Production Classifier. Multiple Classifier Systems 2005: 376-386 - 2004
- [c2]Piero P. Bonissone, Kai Goebel, Weizhong Yan:
Classifier Fusion Using Triangular Norms. Multiple Classifier Systems 2004: 154-163 - 2002
- [j7]Jussi Karlgren, Pentti Kanerva, Björn Gambäck, Kenneth D. Forbus, Kagan Tumer, Peter Stone, Kai Goebel, Gaurav S. Sukhatme, Tucker R. Balch, Bernd Fischer, Doug Smith, Sanda M. Harabagiu, Vinay K. Chaudri, Mike Barley, Hans W. Guesgen, Thomas F. Stahovich, Randall Davis, James A. Landay:
The 2002 AAAI Spring Symposium Series. AI Mag. 23(4): 101-106 (2002) - 2001
- [j6]Alice M. Agogino, Piero P. Bonissone, Kai Goebel, George J. Vachtsevanos:
Special Issue: AI in equipment service. Artif. Intell. Eng. Des. Anal. Manuf. 15(4): 265-266 (2001) - [j5]Piero P. Bonissone, Kai Goebel:
Soft Computing for diagnostics in equipment service. Artif. Intell. Eng. Des. Anal. Manuf. 15(4): 267-279 (2001) - [j4]Kai Goebel:
Architecture and design of a diagnostic information fusion system. Artif. Intell. Eng. Des. Anal. Manuf. 15(4): 335-348 (2001) - [c1]William Cheetham, Anil Varma, Kai Goebel:
Case-Based Reasoning at General Electric. FLAIRS 2001: 93-97 - [p1]Bill Cheetham, Paul Cuddihy, Kai Goebel:
Applications of Soft CBR at General Electric. Soft Computing in Case Based Reasoning 2001: 335-365 - 2000
- [j3]David J. Musliner, Barney Pell, Wolff Dobson, Kai Goebel, Gautam Biswas, Sheila A. McIlraith, Giuseppina C. Gini, Sven Koenig, Shlomo Zilberstein, Weixiong Zhang:
Reports on the AAAI Spring Symposia (March 1999). AI Mag. 21(2): 79-84 (2000) - [j2]Kai F. Goebel, Alice M. Agogino:
Fuzzy Belief Nets. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 8(4): 453-469 (2000)
1990 – 1999
- 1999
- [j1]Piero P. Bonissone, Yu-To Chen, Kai Goebel, Pratap S. Khedkar:
Hybrid soft computing systems: industrial and commercial applications. Proc. IEEE 87(9): 1641-1667 (1999)
Coauthor Index
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