Professor Nikolaos Dervilis
BSc, MSc, PhD
Department of Mechanical Engineering
Professor
+44 114 222 7816
Full contact details
Department of Mechanical Engineering
Room D224, Central Wing
Sir Frederick Mappin Building
Mappin Street
Sheffield
S1 3JD
 Profile

Nikolaos Dervilis is a Professor in the Department of Mechanical Engineering at the University of Sheffield and a member of the Dynamics Research Group (DRG). He studied physics in the National and Kapodistrian University of Athens. Later, he obtained his MSc in Sustainable and Renewable Energy Systems from the University of Edinburgh in the Department of Electronics and Electrical Engineering. He obtained his PhD from the University of Sheffield, Mechanical Engineering Department in the field of machine learning for Structural Health Monitoring (SHM). His expertise focuses on SHM, pattern recognition, data analysis and nonlinear dynamics. He is especially engaged with renewable energy research, particularly wind turbine farms.
 Research interests

Nikolaos’s current research interests include:
 Structural Health Monitoring (SHM): machine learning and pattern recognition techniques for data analysis, structural damage characterisation and nondestructive evaluation methods.
 Onshore and offshore wind farms, sustainable energy systems.
 Data analysis and information learning tools.
 Nonlinear dynamics and advanced signal processing.
 Publications

Journal articles
 Towards a populationinformed approach to the definition of datadriven models for structural dynamics. Mechanical Systems and Signal Processing, 200, 110581110581.
 Identification of piecewiselinear mechanical oscillators via Bayesian model selection and parameter estimation. Mechanical Systems and Signal Processing, 196, 110300110300.
 A fullscale wind turbine blade monitoring campaign: detection of damage initiation and progression using mediumfrequency active vibrations. Structural Health Monitoring.
 A timeevolving digital twin tool for engineering dynamics applications. Mechanical Systems and Signal Processing, 188, 109971109971.
 Reduced order modeling of nonlinear monopile dynamics via an AELSTM scheme. Frontiers in Energy Research, 11.
 A Bayesian method for material identification of composite plates via dispersion curves. Sensors, 23(1).
 On the dynamic properties of statisticallyindependent nonlinear normal modes. Mechanical Systems and Signal Processing, 181, 109510109510.
 On robust riskbased activelearning algorithms for enhanced decision support. Mechanical Systems and Signal Processing, 181, 109502109502.
 Modelling variability in vibrationbased PBSHM via a generalised population form. Journal of Sound and Vibration, 538, 117227117227.
 On Topological Data Analysis for Structural Dynamics: An Introduction to Persistent Homology. ASME Open Journal of Engineering, 1.
 A samplingbased approach for informationtheoretic inspection management. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 478(2262).
 Informative bayesian tools for damage localisation by decomposition of Lamb wave signals. Journal of Sound and Vibration. View this article in WRRO
 On the application of kernelised Bayesian transfer learning to populationbased structural health monitoring. Mechanical Systems and Signal Processing, 167(Part B).
 On the application of generative adversarial networks for nonlinear modal analysis. Mechanical Systems and Signal Processing, 166, 108473108473.
 A populationbased SHM methodology for heterogeneous structures : transferring damage localisation knowledge between different aircraft wings. Mechanical Systems and Signal Processing, 172. View this article in WRRO
 Impact of blade structural and aerodynamic uncertainties on wind turbine loads. Wind Energy.
 Error motion trajectorydriven diagnostics of kinematic and nonkinematic machine tool faults. Mechanical Systems and Signal Processing, 164. View this article in WRRO
 Predicting local material thickness from steadystate ultrasonic wavefield measurements using a convolutional neural network. Ultrasonics.
 Bayesian modelling of multivalued power curves from an operational wind farm. Mechanical Systems and Signal Processing.
 On generative models as the basis for digital twins. DataCentric Engineering, 2.
 Foundations of populationbased SHM, Part IV : the geometry of spaces of structures and their feature spaces. Mechanical Systems and Signal Processing, 157.
 Machine learning approach to model order reduction of nonlinear systems via autoencoder and LSTM networks. Journal of Engineering Mechanics, 147(10).
 Structured machine learning tools for modelling characteristics of guided waves. Mechanical Systems and Signal Processing, 156. View this article in WRRO
 On the transfer of damage detectors between structures: an experimental case study. Journal of Sound and Vibration, 501.
 Overcoming the problem of repair in structural health monitoring: Metricinformed transfer learning. Journal of Sound and Vibration, 116245116245.
 Comparing approaches for multiaxis kinematic positioning in machine tools. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 095440542110196.
 A probabilistic riskbased decision framework for structural health monitoring. Mechanical Systems and Signal Processing, 150, 107339107339.
 Probabilistic inference for structural health monitoring: new modes of learning from data. ASCEASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(1). View this article in WRRO
 Foundations of populationbased SHM, part III : heterogeneous populations – mapping and transfer. Mechanical Systems and Signal Processing, 149. View this article in WRRO
 Foundations of populationbased SHM, part II : heterogeneous populations – graphs, networks, and communities. Mechanical Systems and Signal Processing, 148. View this article in WRRO
 Foundations of populationbased SHM, Part I : homogeneous populations and forms. Mechanical Systems and Signal Processing, 148. View this article in WRRO
 Normalising Flows and Nonlinear Normal Modes. IFACPapersOnLine, 54(7), 655660.
 Towards the probabilistic analysis of small bowel capsule endoscopy features to predict severity of duodenal histology in patients with villous atrophy. Journal of Medical Systems, 44(11).
 Machine learning at the interface of structural health monitoring and nondestructive evaluation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 378(2182), 2019058120190581.
 Machining centre performance monitoring with calibrated artefact probing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.
 Towards semisupervised and probabilistic classification in structural health monitoring. Mechanical Systems and Signal Processing, 140. View this article in WRRO
 Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression. Renewable Energy, 148, 11241136. View this article in WRRO
 Damage detection in operational wind turbine blades using a new approach based on machine learning. Renewable Energy.
 Probabilistic active learning : an online framework for structural health monitoring. Mechanical Systems and Signal Processing, 134. View this article in WRRO
 Book Review. Journal of Sound and Vibration, 458, 347348.
 Model selection and parameter estimation of dynamical systems using a novel variant of approximate Bayesian computation. Mechanical Systems and Signal Processing, 122, 364386. View this article in WRRO
 Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from highdimensional data. Journal of Sound and Vibration. View this article in WRRO
 A Bayesian nonparametric clustering approach for semisupervised Structural Health Monitoring. Mechanical Systems and Signal Processing, 119, 100119. View this article in WRRO
 Active learning for semisupervised structural health monitoring. Journal of Sound and Vibration, 437, 373388. View this article in WRRO
 On evolutionary system identification with applications to nonlinear benchmarks. Mechanical Systems and Signal Processing, 112, 194232. View this article in WRRO
 Nonlinear modal analysis via nonparametric machine learning tools.. Strain: an international journal for experimental mechanics. View this article in WRRO
 Model selection and parameter estimation in structural dynamics using approximate Bayesian computation. Mechanical Systems and Signal Processing, 99, 306325. View this article in WRRO
 Performance monitoring of a wind turbine using extreme function theory. Renewable Energy, 113, 14901502. View this article in WRRO
 Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo. Frontiers in Built Environment, 3. View this article in WRRO
 A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview. Progress in Aerospace Sciences, 90, 111. View this article in WRRO
 Robust methods for outlier detection and regression for SHM applications.. International Journal of Sustainable Materials and Structural Systems. View this article in WRRO
 A Nonlinear Manifold Strategy for SHM Approaches. Strain, 51(4), 324331. View this article in WRRO
 On robust regression analysis as a means of exploring environmental and operational conditions for SHM data. Journal of Sound and Vibration, 347, 279296. View this article in WRRO
 A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm. IEEE Transactions on Industrial Electronics, 62(10), 66366644. View this article in WRRO
 Aspects of structural health and condition monitoring of offshore wind turbines. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2035), 2014007520140075. View this article in WRRO
 Robust methods of inclusive outlier analysis for structural health monitoring. JOURNAL OF SOUND AND VIBRATION, 333(20), 51815195.
 Envelope analysis using the TeagerKaiser Energy operator for condition monitoring of a wind turbine bearing. Applied Mechanics and Materials, 564, 170175.
 An SHM view of a CFD model of Lillgrund wind farm. Applied Mechanics and Materials, 564, 164169.
 Damage detection in RAPTOR telescope systems using timefrequency analysis methods. Key Engineering Materials, 588, 4353.
 Machine learning applications for a wind turbine blade under continuous fatigue loading. Key Engineering Materials, 588, 166174.
 On damage diagnosis for a wind turbine blade using pattern recognition. JOURNAL OF SOUND AND VIBRATION, 333(6), 18331850.
 Advanced tools for damage detection in wind turbines. Key Engineering Materials, 569570, 547554.
 Comparative study of robust novelty detection techniques. Key Engineering Materials, 569570, 11091115.
 Condition monitoring of a wind turbine gearbox using the empirical mode decomposition method and outlier analysis. Proceedings of the 6th European Workshop  Structural Health Monitoring 2012, EWSHM 2012, 2, 13161323.
 Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations. Wind Energy.
 Using Non‐contact Measurement of Water Surface Dynamics to Estimate River Discharge. Water Resources Research.
 On statistic alignment for domain adaptation in structural health monitoring. Structural Health Monitoring, 147592172211104147592172211104.
 Domainadapted Gaussian mixture models for populationbased structural health monitoring. Journal of Civil Structural Health Monitoring.
 A Bayesian Approach for Shaft Centre Localisation in Journal Bearings. Mechanical Systems and Signal Processing.
 Autonomous ultrasonic inspection using bayesian optimisation and robust outlier analysis. Mechanical Systems and Signal Processing, 145. View this article in WRRO
 A Brief Introduction to Recent Developments in PopulationBased Structural Health Monitoring. Frontiers in Built Environment, 6.
 Equation discovery for nonlinear dynamical systems: a Bayesian viewpoint. Mechanical Systems and Signal Processing.
 On riskbased active learning for structural health monitoring.
Chapters
 A Topological Analysis of Cointegrated Data: A Z24 Bridge Case Study, Lecture Notes in Civil Engineering (pp. 10951106). Springer International Publishing
 On Modelling Statistically Independent Nonlinear Normal Modes with Gaussian Process NARX Models, Nonlinear Structures & Systems, Volume 1 (pp. 135147). Springer International Publishing
 Approximate Bayesian Inference for PiecewiseLinear Stiffness Systems, Nonlinear Structures & Systems, Volume 1 (pp. 165175). Springer International Publishing
 On the Use of CycleConsistent Generative Adversarial Networks for Nonlinear Modal Analysis, Topics in Modal Analysis & Parameter Identification, Volume 8 (pp. 4557). Springer International Publishing
 On the Use of Variational Autoencoders for Nonlinear Modal Analysis, Nonlinear Structures & Systems, Volume 1 (pp. 297300). Springer International Publishing
 Multilayer Input Deep Learning Applied to Ultrasonic Wavefield Measurements, Data Science in Engineering, Volume 9 (pp. 143156). Springer International Publishing
 Structural Health Monitoring and Damage Identification, Handbook of Experimental Structural Dynamics (pp. 9891061). Springer New York
 Partially Supervised Learning for DataDriven Structural Health Monitoring, Structural Integrity (pp. 389411). Springer International Publishing
 PopulationBased Structural Health Monitoring, Structural Integrity (pp. 413435). Springer International Publishing
 Structural Health Monitoring and Damage Identification, Handbook of Experimental Structural Dynamics (pp. 172). Springer New York
 On Topological Data Analysis for SHM: An Introduction to Persistent Homology, Data Science in Engineering, Volume 9 (pp. 169184). Springer International Publishing
 On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks, Data Science in Engineering, Volume 9 (pp. 3546). Springer International Publishing
 On an Application of Graph Neural Networks in PopulationBased SHM, Data Science in Engineering, Volume 9 (pp. 4763). Springer International Publishing
 Application of a UNet Convolutional Neural Network to Ultrasonic Wavefield Measurements for Defect Characterization, Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6 (pp. 167181). Springer International Publishing
 Transferring Damage Detectors Between Tailplane Experiments, Data Science in Engineering, Volume 9 (pp. 199211).
 New Modes of Inference for Probabilistic SHM, Lecture Notes in Civil Engineering (pp. 415426). Springer International Publishing
 On Partitioning of an SHM Problem and Parallels with Transfer Learning, Topics in Modal Analysis & Testing, Volume 8 (pp. 4150). Springer International Publishing
 Towards PopulationBased Structural Health Monitoring, Part IV: Heterogeneous Populations, Transfer and Mapping, Model Validation and Uncertainty Quantification, Volume 3 (pp. 187199). Springer International Publishing
 Towards PopulationBased Structural Health Monitoring, Part I: Homogeneous Populations and Forms, Model Validation and Uncertainty Quantification, Volume 3 (pp. 287302). Springer International Publishing
 Kernelised Bayesian Transfer Learning for PopulationBased Structural Health Monitoring, Model Validation and Uncertainty Quantification, Volume 3 (pp. 209215). Springer International Publishing
 An Evolutionary Approach to Learning Neural Networks for Structural Health Monitoring, Model Validation and Uncertainty Quantification, Volume 3 (pp. 237246). Springer International Publishing
 Structural Health Monitoring and Damage Identification, Handbook of Experimental Structural Dynamics (pp. 172).
 Investigating Engineering Data by Probabilistic Measures, Special Topics in Structural Dynamics & Experimental Techniques, Volume 5 (pp. 7781). Springer International Publishing
Conference proceedings papers
 A Bayesian Approach to LambWave Dispersion Curve Material Identification in Composite Plates (pp 139149)
 On the Application of Partial Domain Adaptation for PBSHM (pp 408418)
 Semisupervised riskbased active learning using inspection and maintenance information. Proceedings of ISMA 2022International Conference on Noise and Vibration Engineering and USD 2022International Conference on Uncertainty in Structural Dynamics
 View this article in WRRO On the Application of Variational Auto Encoders (VAE) for Damage Detection in Rolling Element Bearings. Proceedings of the Thirteenth International Workshop on Structural Health Monitoring, IWSHM 2021 (pp 388397). Lancaster, PA,. USA, 15 March 2022  17 March 2022.
 ON AN APPLICATION OF GENERATIVE ADVERSARIAL NETWORKS ON REMAINING LIFETIME ESTIMATION. Proceedings of the 13th International Workshop on Structural Health Monitoring
 INVESTIGATING EXPERIMENTAL REPEATABILITY AND FEATURE CONSISTENCY IN VIBRATIONBASED SHM. Proceedings of the 13th International Workshop on Structural Health Monitoring
 INVESTIGATING THE EFFECTS OF AMBIENT TEMPERATURE ON FEATURE CONSISTENCY IN VIBRATIONBASED SHM. Proceedings of the 13th International Workshop on Structural Health Monitoring
 NONLINEAR REDUCED ORDER MODELLING OF SOIL STRUCTURE INTERACTION EFFECTS VIA LSTM AND AUTOENCODER NEURAL NETWORKS. Proceedings of the 13th International Workshop on Structural Health Monitoring
 ON A POPULATIONBASED STRUCTURAL HEALTH MONITORING FRAMEWORK: AN AEROSPACE CASE STUDY. Proceedings of the 13th International Workshop on Structural Health Monitoring
 ON THE APPLICATION OF TOPOLOGICAL DATA ANALYSIS: A Z24 BRIDGE CASE STUDY. Proceedings of the 13th International Workshop on Structural Health Monitoring
 ON NORMALISATION FOR DOMAIN ADAPTATION IN POPULATIONBASED STRUCTURAL HEALTH MONITORING. Proceedings of the 13th International Workshop on Structural Health Monitoring
 On Affine Symbolic Regression Trees for the Solution of Functional Problems (pp 95108)
 On the Application of the Generating Series for Nonlinear Systems with Polynomial Stiffness (pp 135149)
 An Initial Concept for an ErrorBased Digital Twin Framework for Dynamics Applications. Conference Proceedings of the Society for Experimental Mechanics Series (pp 8189)
 On the application of heterogeneous transfer learning to populationbased structural health monitoring. Data Science in Engineering, Volume 9 : Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021 (pp 8798). Virtual conference, 8 February 2021  11 February 2021.
 Challenges for SHM from structural repairs : an outlierinformed domain adaptation approach. Data Science in Engineering, Volume 9 : Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021 (pp 7586). Virtual conference, 8 February 2021  11 February 2021.
 Decomposition of multimode signals using dispersion curves and Bayesian linear regression. Health Monitoring of Structural and Biological Systems XV, 22 March 2021  27 March 2021.
 On DomainAdapted Gaussian Mixture Models for PopulationBased Structural Health Monitoring. International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII, Vol. 2021June (pp 663670)
 Automated Feature Extraction for Damage Detection: A Pseudofault Framework for Populationbased SHM. International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII, Vol. 2021June (pp 655661)
 On the use of nonlinear normal modes for nonlinear reduced order modelling. EURODYN 2020  XI International Conference on Structural Dynamics, Proceedings, Vol. II (pp 38653877). Athens, Greece, 23 November 2020  26 November 2020.
 Towards populationbased structural health monitoring, Part II : heterogeneous populations and structures as graphs. Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020, Vol. 8 (pp 177187). Houston, TX, USA, 10 February 2020  13 February 2020. View this article in WRRO
 Modelling of Guided Waves in a Composite Plate Through a Combination of Physical Knowledge and Regression Analysis (pp 109114)
 View this article in WRRO Towards populationbased structural health monitoring, Part III: Graphs, networks and communities. Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics
 Lamb wave mode separation using dispersion curves. Proceedings of ISMA 2020  International Conference on Noise and Vibration Engineering and USD 2020  International Conference on Uncertainty in Structural Dynamics (pp 28912898)
 Preface. Conference Proceedings of the Society for Experimental Mechanics Series (pp v)
 AN APPLICATION OF GENERATIVE ADVERSARIAL NETWORKS IN STRUCTURAL HEALTH MONITORING. XI International Conference on Structural Dynamics, 23 November 2020  26 November 2020.
 A NEAT APPROACH TO STRUCTURAL HEALTH MONITORING. XI International Conference on Structural Dynamics, 23 November 2020  26 November 2020.
 Uncertainty quantification framework for structural model of wind turbine blades. Proceedings of the International Conference on Structural Dynamic , EURODYN, Vol. 2 (pp 39113920)
 On the quantification of structural uncertainties of blades and their effect on wind turbine loads. Proceedings of ISMA 2020  International Conference on Noise and Vibration Engineering and USD 2020  International Conference on Uncertainty in Structural Dynamics (pp 38533862)
 A probabilistic framework for online structural health monitoring : active learning from machining data streams. Journal of Physics: Conference Series, Vol. 1264. Valpre, Lyon, France, 15 April 2019  17 April 2019. View this article in WRRO
 Efficient parameter identification and model selection in nonlinear dynamical systems via sparse Bayesian learning. Journal of Physics: Conference Series, Vol. 1264(1). Valpre, Lyon, France, 15 April 2019  17 April 2019. View this article in WRRO
 View this article in WRRO A nonlinear robust outlier detection approach for SHM. 8th IOMAC  International Operational Modal Analysis Conference, Proceedings (pp 107114)
 An Efficient LikelihoodFree Bayesian Computation for Model Selection and Parameter Estimation Applied to Structural Dynamics (pp 141151)
 Active Learning Approaches to Structural Health Monitoring (pp 157159)
 Statistical analysis of damage indicators based on ultrasonic testing with embedded piezoelectric transducers. 9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019 (pp 251262)
 Machine Learning for Energy Load Forecasting. Journal of Physics: Conference Series, Vol. 1106(1), 2 July 2018  4 July 2018. View this article in WRRO
 Outlier ensembles: An alternative robust method for inclusive outlier analysis with structural health monitoring data. 9th European Workshop on Structural Health Monitoring, EWSHM 2018
 A semisupervised bayesian nonparametric approach to damage detection. 9th European Workshop on Structural Health Monitoring, EWSHM 2018
 Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data. Safety and Reliability  Safe Societies in a Changing World  Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018 (pp 30533062)
 Outlier analysis under uncertainty: Applications to structural health monitoring. Proceedings of ISMA 2018  International Conference on Noise and Vibration Engineering and USD 2018  International Conference on Uncertainty in Structural Dynamics (pp 49854999)
 On the use of pseudodamage to represent damage to structures in populationbased Structural Health Monitoring. Proceedings of ISMA 2018  International Conference on Noise and Vibration Engineering and USD 2018  International Conference on Uncertainty in Structural Dynamics (pp 37093722)
 View this article in WRRO ABCNS: a new computational inference method applied to parameter estimation and model selection in structural dynamics. 23 Congrès Français de Mécanique, 1 September 2017.
 Is it worth changing pattern recognition methods for structural health monitoring?. Journal of Physics: Conference Series, Vol. 842 View this article in WRRO
 Aspects of computational intelligence in structural dynamics: Structural health monitoring. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 27 November 2017  1 December 2017.
 Aspects of Computational Intelligence in Structural Dynamics: Structural Health Monitoring. 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp 16771683)
 Wind Turbine Health Monitoring: Current and Future Trends with an Active Learning Twist (pp 119129)
 InProcess Monitoring of Automated Carbon Fibre Tape Layup Using Ultrasonic Guided Waves (pp 179188)
 Preface. Conference Proceedings of the Society for Experimental Mechanics Series (pp v)
 View this article in WRRO Identification of nonlinear dynamical systems using approximate Bayesian computation based on a sequential Monte Carlo sampler. Proceedings of ISMA 2016  International Conference on Noise and Vibration Engineering and USD2016  International Conference on Uncertainty in Structural Dynamics (pp 25512565)
 View this article in WRRO An exploratory study of the suitability of a wind turbine blade as a nonlinear demonstrator. Proceedings of ISMA 2016  International Conference on Noise and Vibration Engineering and USD2016  International Conference on Uncertainty in Structural Dynamics (pp 40694080)
 View this article in WRRO On the usage of active learning for SHM. Proceedings of ISMA 2016  International Conference on Noise and Vibration Engineering and USD2016  International Conference on Uncertainty in Structural Dynamics (pp 40334043)
 Exploring Environmental and Operational Variations in SHM Data Using Heteroscedastic Gaussian Processes (pp 145153)
 Simplifying Transformations for Nonlinear Systems: Part II, Statistical Analysis of Harmonic Cancellation (pp 321326) View this article in WRRO
 Simplifying Transformations for Nonlinear Systems: Part I, An OptimisationBased Variant of Normal Form Analysis (pp 315320) View this article in WRRO
 View this article in WRRO Experimental evaluation of environmental effects on a polymercoated aluminium structure: A timeseries analysis and pattern recognition approach. Proceedings of ISMA 2016  International Conference on Noise and Vibration Engineering and USD2016  International Conference on Uncertainty in Structural Dynamics (pp 32953307)
 Structural Health Monitoring: from Structures to SystemsofSystems ★ ★The support of the UK Engineering and Physical Sciences Research Council (EPSRC) through grant reference numbers EP/J016942/1 and EP/K003836/2, and that of the EU Framework 7 Programme for the ITN project SYSWIND, is gratefully acknowledged.. IFACPapersOnLine, Vol. 48(21) (pp 117)
 Wind turbine structural health monitoring: A short investigation based on SCADA data. 7th European Workshop on Structural Health Monitoring, EWSHM 2014  2nd European Conference of the Prognostics and Health Management (PHM) Society (pp 512519)
 View this article in WRRO Nonlinear modal analysis using pattern recognition. Proceedings of ISMA 2014  International Conference on Noise and Vibration Engineering and USD 2014  International Conference on Uncertainty in Structural Dynamics (pp 30173028)
 Nonlinear robust regression analysis as a means of exploring SHM data. Proceedings of ISMA 2014  International Conference on Noise and Vibration Engineering and USD 2014  International Conference on Uncertainty in Structural Dynamics (pp 513525)
 AutoAssociation and Novelty Detection: Truths and Myths?. STRUCTURAL HEALTH MONITORING 2013, VOLS 1 AND 2 (pp 243250)
 Impact damage detection for composite material typical of wind turbine blades using novelty detection. Proceedings of the 6th European Workshop  Structural Health Monitoring 2012, EWSHM 2012, Vol. 2 (pp 12871296)
 Structural Health Monitoring of composite material typical of wind turbine blades by novelty detection on vibration response. Key Engineering Materials, Vol. 518 (pp 319327)
 Novelty detection applied to vibration data from a CX100 wind turbine blade under fatigue loading.. MODERN PRACTICE IN STRESS AND VIBRATION ANALYSIS 2012 (MPSVA 2012), Vol. 382
 Use of the TeagerKaiser energy operator for condition monitoring of a wind turbine gearbox. PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING (ISMA2012) / INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS (USD2012) (pp 42554268)
 On damage detection in wind turbine gearboxes using outlier analysis. Proceedings of SPIE  The International Society for Optical Engineering, Vol. 8343
 Damage detection in carbon composite material typical of wind turbine blades using autoassociative neural networks. Proceedings of SPIE  The International Society for Optical Engineering, Vol. 8348
 Gaussian Processes for Structural Health Monitoring of Wind Turbine Blades. Structural Health Monitoring 2019, 24 October 2018  26 October 2018. View this article in WRRO
 Experimental Validation of the PopulationForm to Represent NominallyIdentical Systems. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Structural Health Monitoring: A Review of Uncertainty Quantification Methods in Wind Turbine Systems. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Damage Classification Using Labelled and Unlabelled Measurements. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Applying the Concept of Complexity to Structural Health Monitoring. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Health Monitoring of Composite Structures by Combining Ultrasonic Wave Data. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Assessing the Likelihood of Damage at the Start of a Structural Health Monitoring Campaign. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Automated Fault Diagnosis with Calibrated Artefact Probing. Structural Health Monitoring 2019, 24 October 2018  26 October 2018.
 Towards a Populationbased SHM: A Case Study on an Offshore Wind Farm. Structural Health Monitoring 2015
 Extreme Function Theory for SHM: A Case Study for Wind Turbines. Structural Health Monitoring 2015
 View this article in WRRO A Gaussian Process Form for PopulationBased Structural Health Monitoring. DAMAS 2019
 On the Structural Health Monitoring of Operational Wind Turbine Blades. Structural Health Monitoring 2017, 12 September 2017  14 September 2017. View this article in WRRO
 View this article in WRRO A riskbased active learning approach to inspection scheduling. Proceedings of the 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure
Presentations
 Recent Advances in Approximate Bayesian Computation Methodology (Application in structural dynamics)..
Preprints
 Towards a populationinformed approach to the definition of datadriven models for structural dynamics, arXiv.
 A decision framework for selecting informationtransfer strategies in populationbased SHM, arXiv.
 On the hierarchical Bayesian modelling of frequency response functions.
 When is an SHM problem a MultiTaskLearning problem?, arXiv.
 VpROM: A novel Variational AutoEncoderboosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems, arXiv.
 Better Together: Using Multitask Learning to Improve Feature Selection within Structural Datasets, arXiv.
 A MetaLearning Approach to PopulationBased Modelling of Structures.
 Towards a PopulationInformed Approach to the Definition of DataDriven Models for Structural Dynamics, Elsevier BV.
 On the application of the generating series for nonlinear systems with polynomial stiffness, arXiv.
 On topological data analysis for structural dynamics: an introduction to persistent homology, arXiv.
 On the application of topological data analysis: a Z24 Bridge case study.
 A topological analysis of cointegrated data: a Z24 Bridge case study, arXiv.
 A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves, arXiv.
 On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation.
 Mitigating sampling bias in riskbased active learning via an EM algorithm.
 Improving decisionmaking via riskbased active learning: Probabilistic discriminative classifiers.
 A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes.
 On statistic alignment for domain adaptation in structural health monitoring.
 Informative Bayesian Tools for Damage Localisation by Decomposition of Lamb Wave Signals.
 A Bayesian Approach for Shaft Centre Localisation in Journal Bearings.
 Modelling variability in vibrationbased PBSHM via a generalised population form, arXiv.
 On generative models as the basis for digital twins, arXiv.
 On partitioning of an SHM problem and parallels with transfer learning.
 On generating parametrised structural data using conditional generative adversarial networks.
 On an application of graph neural networks in population based SHM.
 Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects via LSTM and Autoencoder Neural Networks.
 On the application of generative adversarial networks for nonlinear modal analysis.
 View this article in WRRO On robust riskbased activelearning algorithms for enhanced decision support, arXiv.
 Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm, arXiv.
 Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks, arXiv.
 On riskbased active learning for structural health monitoring, arXiv.
 Foundations of PopulationBased SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces, arXiv.
 Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data, arXiv.
 Damage detection in operational wind turbine blades using a new approach based on machine learning, arXiv.
 Structured Machine Learning Tools for Modelling Characteristics of Guided Waves, arXiv.
 A probabilistic riskbased decision framework for structural health monitoring, arXiv.
 On the use of Nonlinear Normal Modes for Nonlinear Reduced Order Modelling, arXiv.
 A Bayesian Method for Material Identification of Composite Plates Via Dispersion Curves.