| 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 | 197 | 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 | 169 | 2015 |
| Foundations of population-based SHM, Part I: Homogeneous populations and forms LA Bull, PA Gardner, J Gosliga, TJ Rogers, N Dervilis, EJ Cross, ... Mechanical systems and signal processing 148, 107141, 2021 | 152 | 2021 |
| Foundations of population-based SHM, Part III: Heterogeneous populations–Mapping and transfer P Gardner, LA Bull, J Gosliga, N Dervilis, K Worden Mechanical Systems and Signal Processing 149, 107142, 2020 | 150 | 2020 |
| 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 Dervili, ... Safety and Reliability–Safe Societies in a Changing World, 3053-3061, 2018 | 141 | 2018 |
| Foundations of Population-based SHM, Part II: Heterogeneous populations–Graphs, networks, and communities J Gosliga, PA Gardner, LA Bull, N Dervilis, K Worden Mechanical Systems and Signal Processing 148, 107144, 2021 | 134 | 2021 |
| 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 | 115 | 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 | 114 | 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 | 93 | 2019 |
| 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 | 92 | 2018 |
| 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 | 87 | 2015 |
| Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian process regression TJ Rogers, P Gardner, N Dervilis, K Worden, AE Maguire, E Papatheou, ... Renewable Energy 148, 1124-1136, 2020 | 84 | 2020 |
| 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 | 78 | 2018 |
| 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 | 74 | 2014 |
| Towards semi-supervised and probabilistic classification in structural health monitoring LA Bull, K Worden, N Dervilis Mechanical Systems and Signal Processing 140, 106653, 2020 | 72 | 2020 |
| Probabilistic active learning: an online framework for structural health monitoring LA Bull, TJ Rogers, C Wickramarachchi, EJ Cross, K Worden, N Dervilis Mechanical Systems and Signal Processing 134, 106294, 2019 | 71 | 2019 |
| Machine learning at the interface of structural health monitoring and non-destructive evaluation P Gardner, R Fuentes, N Dervilis, C Mineo, SG Pierce, EJ Cross, ... Philosophical Transactions of the Royal Society A 378 (2182), 20190581, 2020 | 69 | 2020 |
| 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 | 68 | 2017 |
| 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 | 65 | 2018 |
| Machine learning approach to model order reduction of nonlinear systems via autoencoder and LSTM networks T Simpson, N Dervilis, E Chatzi Journal of Engineering Mechanics 147 (10), 04021061, 2021 | 64 | 2021 |
Keith WordenUniversity of SheffieldVerified email at sheffield.ac.uk
