| On damage diagnosis for a wind turbine blade using pattern recognition N Dervilis, M Choi, SG Taylor, RJ Barthorpe, G Park, CR Farrar, K Worden Journal of sound and vibration 333 (6), 1833-1850, 2014 | 98 | 2014 |
| On robust regression analysis as a means of exploring environmental and operational conditions for SHM data N Dervilis, K Worden, EJ Cross Journal of Sound and Vibration 347, 279-296, 2015 | 81 | 2015 |
| A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview KM Holford, MJ Eaton, JJ Hensman, R Pullin, SL Evans, N Dervilis, ... Progress in Aerospace Sciences 90, 1-11, 2017 | 42 | 2017 |
| Aspects of structural health and condition monitoring of offshore wind turbines I Antoniadou, N Dervilis, E Papatheou, AE Maguire, K Worden Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2015 | 41 | 2015 |
| Robust methods of inclusive outlier analysis for structural health monitoring N Dervilis, EJ Cross, RJ Barthorpe, K Worden Journal of Sound and Vibration 333 (20), 5181-5195, 2014 | 38 | 2014 |
| A performance monitoring approach for the novel Lillgrund offshore wind farm E Papatheou, N Dervilis, AE Maguire, I Antoniadou, K Worden IEEE Transactions on Industrial Electronics 62 (10), 6636-6644, 2015 | 30 | 2015 |
| Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data I Abdallah, V Dertimanis, H Mylonas, K Tatsis, E Chatzi, N Dervilis, ... Safety and Reliability–Safe Societies in a Changing World, 3053-3061, 2018 | 26 | 2018 |
| Model selection and parameter estimation in structural dynamics using approximate Bayesian computation AB Abdessalem, N Dervilis, D Wagg, K Worden Mechanical Systems and Signal Processing 99, 306-325, 2018 | 23 | 2018 |
| On evolutionary system identification with applications to nonlinear benchmarks K Worden, RJ Barthorpe, EJ Cross, N Dervilis, GR Holmes, G Manson, ... Mechanical Systems and Signal Processing 112, 194-232, 2018 | 19 | 2018 |
| Performance monitoring of a wind turbine using extreme function theory E Papatheou, N Dervilis, AE Maguire, C Campos, I Antoniadou, K Worden Renewable Energy 113, 1490-1502, 2017 | 16 | 2017 |
| Structural health monitoring: from structures to systems-of-systems K Worden, EJ Cross, N Dervilis, E Papatheou, I Antoniadou IFAC-papersonline 48 (21), 1-17, 2015 | 14 | 2015 |
| A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring TJ Rogers, K Worden, R Fuentes, N Dervilis, UT Tygesen, EJ Cross Mechanical Systems and Signal Processing 119, 100-119, 2019 | 13 | 2019 |
| Active learning for semi-supervised structural health monitoring L Bull, K Worden, G Manson, N Dervilis Journal of Sound and Vibration 437, 373-388, 2018 | 13 | 2018 |
| Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading N Dervilis, M Choi, I Antoniadou, KM Farinholt, SG Taylor, RJ Barthorpe, ... Journal of Physics-Conference Series 382 (1), 012047, 2012 | 13 | 2012 |
| Wind turbine structural health monitoring: a short investigation based on SCADA data E Papatheou, N Dervilis, E Maguire, K Worden | 12 | 2014 |
| Condition monitoring of a wind turbine gearbox using the empirical mode decomposition method and outlier analysis I Antoniadou, G Manson, N Dervilis, T Barszcz, W Staszewski, K Worden Sixth European Workshop on Structural Health Monitoring, Dresden, Germany 3 (6), 2012 | 11 | 2012 |
| Automatic kernel selection for gaussian processes regression with approximate bayesian computation and sequential monte carlo AB Abdessalem, N Dervilis, DJ Wagg, K Worden Frontiers in Built Environment 3, 52, 2017 | 10 | 2017 |
| Exploring environmental and operational variations in SHM data using heteroscedastic Gaussian processes N Dervilis, H Shi, K Worden, EJ Cross Dynamics of Civil Structures, Volume 2, 145-153, 2016 | 9 | 2016 |
| Machine learning applications for a wind turbine blade under continuous fatigue loading N Dervilis, M Choi, I Antoniadou, KM Farinholt, SG Taylor, RJ Barthorpe, ... Key Engineering Materials 588, 166-174, 2014 | 9 | 2014 |
| A machine learning approach to structural health monitoring with a view towards wind turbines N Dervilis University of Sheffield, 2013 | 9 | 2013 |
Keith WordenUniversity of SheffieldVerified email at sheffield.ac.uk
