| Uncertainty Quantification and Model Validation of Fatigue Crack Growth Prediction S Sankararaman, Y Ling, S Mahadevan Engineering Fracture Mechanics 78 (7), 1487-1504, 2011 | 240 | 2011 |
| Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling M Baptista, S Sankararaman, IP de Medeiros, C Nascimento Jr, ... Computers & Industrial Engineering 115, 41-53, 2018 | 231 | 2018 |
| 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 | 168 | 2011 |
| Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction S Sankararaman Mechanical Systems and Signal Processing 52, 228-247, 2015 | 164 | 2015 |
| Resilience-based network design under uncertainty X Zhang, S Mahadevan, S Sankararaman, K Goebel Reliability Engineering & System Safety 169, 364-379, 2018 | 134 | 2018 |
| Separating the contributions of variability and parameter uncertainty in probability distributions S Sankararaman, S Mahadevan Reliability Engineering & System Safety 112, 187-199, 2013 | 134 | 2013 |
| 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 | 131 | 2015 |
| 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 | 124 | 2014 |
| Prognostics: The science of making predictions K Goebel, MJ Daigle, A Saxena, I Roychoudhury, S Sankararaman, ... | 101 | 2017 |
| Concepts of Airspace Structures and System Analysis for UAS Traffic flows for Urban Areas DS Jang, CA Ippolito, S Sankararaman, V Stepanyan AIAA Information Systems-AIAA Infotech@ Aerospace, 0449, 2017 | 91 | 2017 |
| Bayesian methodology for diagnosis uncertainty quantification and health monitoring S Sankararaman, S Mahadevan Structural Control and Health Monitoring, 2011 | 88 | 2011 |
| Model validation under epistemic uncertainty S Sankararaman, S Mahadevan Reliability Engineering & System Safety 96 (9), 1232-1241, 2011 | 87 | 2011 |
| 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 | 86 | 2015 |
| Why is the Remaining Useful Life Prediction Uncertain? S Sankararaman, K Goebel Annual Conference of the Pr ognostics and Health Management Society, USA, 2013 | 86 | 2013 |
| A neural network filtering approach for similarity-based remaining useful life estimation O Bektas, JA Jones, S Sankararaman, I Roychoudhury, K Goebel The International Journal of Advanced Manufacturing Technology 101 (1), 87-103, 2019 | 82 | 2019 |
| Likelihood-based approach to multidisciplinary analysis under uncertainty S Sankararaman, S Mahadevan | 77 | 2012 |
| Uncertainty in prognostics and systems health management S Sankararaman, K Goebel International journal of prognostics and health management 6 (4), 2015 | 75 | 2015 |
| Uncertainty in prognostics and systems health management S Sankararaman, K Goebel International journal of prognostics and health management 6 (4), 2015 | 75 | 2015 |
| 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 | 73 | 2015 |
| Methodologies for system-level remaining useful life prediction H Khorasgani, G Biswas, S Sankararaman Reliability Engineering & System Safety 154, 8-18, 2016 | 72 | 2016 |
Sankaran MahadevanJohn R. Murray Sr. Professor of EngineeringVerified email at vanderbilt.edu
