Shankar Sankararaman
Shankar Sankararaman
Data Scientist Lead/Manager at PwC, Former Research Scientist at NASA Ames
Verified email at pwc.com - Homepage
TitleCited byYear
Uncertainty Quantification and Model Validation of Fatigue Crack Growth Prediction
S Sankararaman, Y Ling, S Mahadevan
Engineering Fracture Mechanics 78 (7), 1487-1504, 2011
1492011
Likelihood-based representation of epistemic uncertainty due to sparse point data and/or interval data
S Sankararaman, S Mahadevan
Reliability Engineering & System Safety 96 (7), 814-824, 2011
1132011
Separating the contributions of variability and parameter uncertainty in probability distributions
S Sankararaman, S Mahadevan
Reliability Engineering & System Safety 112, 187-199, 2013
892013
Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction
S Sankararaman
Mechanical Systems and Signal Processing 52, 228-247, 2015
882015
Uncertainty quantification in remaining useful life prediction using first-order reliability methods
S Sankararaman, MJ Daigle, K Goebel
IEEE Transactions on Reliability 63 (2), 603-619, 2014
742014
Model validation under epistemic uncertainty
S Sankararaman, S Mahadevan
Reliability Engineering & System Safety 96 (9), 1232-1241, 2011
602011
Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems
S Sankararaman, S Mahadevan
Reliability Engineering & System Safety 138, 194-209, 2015
542015
Bayesian methodology for diagnosis uncertainty quantification and health monitoring
S Sankararaman, S Mahadevan
Structural Control and Health Monitoring, 2011
542011
Likelihood-based approach to multidisciplinary analysis under uncertainty
S Sankararaman, S Mahadevan
Journal of Mechanical Design 134 (3), 031008, 2012
522012
Condition-based prediction of time-dependent reliability in composites
J Chiachío, M Chiachío, S Sankararaman, A Saxena, K Goebel
Reliability Engineering & System Safety 142, 134-147, 2015
482015
Bayesian model selection and parameter estimation for fatigue damage progression models in composites
J Chiachío, M Chiachío, A Saxena, S Sankararaman, G Rus, K Goebel
International Journal of Fatigue 70, 361-373, 2015
452015
Analytical algorithms to quantify the uncertainty in remaining useful life prediction
S Sankararaman, M Daigle, A Saxena, K Goebel
2013 IEEE Aerospace Conference, 1-11, 2013
442013
Uncertainty Quantification and Integration in Engineering Systems
S Sankararaman
Vanderbilt University, 2012
442012
Uncertainty Quantification in Fatigue Crack Growth Prognosis
S Sankararaman, Y Ling, C Shantz, S Mahadevan
International Journal of Prognostics and Health Management 2 (1), 15, 2011
442011
Uncertainty quantification in fatigue damage prognosis
S Sankararaman, Y Ling, C Shantz, S Mahadevan
1st Annual Conference of the Prognostics and Health Management Society, 2009
432009
Stochastic prediction of fatigue loading using real-time monitoring data
Y Ling, C Shantz, S Mahadevan, S Sankararaman
International Journal of Fatigue 33 (7), 868-879, 2011
422011
Why is the Remaining Useful Life Prediction Uncertain?
S Sankararaman, K Goebel
Annual Conference of the Pr ognostics and Health Management Society, USA, 2013
412013
Inference of equivalent initial flaw size under multiple sources of uncertainty
S Sankararaman, Y Ling, C Shantz, S Mahadevan
International Journal of Fatigue 33 (2), 75-89, 2011
392011
Test resource allocation in hierarchical systems using Bayesian networks
S Sankararaman, K McLemore, S Mahadevan, SC Bradford, LD Peterson
AIAA journal 51 (3), 537-550, 2013
352013
Methodologies for system-level remaining useful life prediction
H Khorasgani, G Biswas, S Sankararaman
Reliability Engineering & System Safety 154, 8-18, 2016
322016
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