Dr Timothy Rogers
MEng, PhD
Department of Mechanical Engineering
Senior Lecturer in Mechanical Engineering
tim.rogers@sheffield.ac.uk
+44 114 222 7820
+44 114 222 7820
Sir Frederick Mappin Building
Full contact details
Dr Timothy Rogers
Department of Mechanical Engineering
Sir Frederick Mappin Building
Mappin Street
Sheffield
S1 3JD
Department of Mechanical Engineering
Sir Frederick Mappin Building
Mappin Street
Sheffield
S1 3JD
- Profile
-
Dr Timothy Rogers is a lecturer in the Dynamics Research Group (DRG), part of the Department of Mechanical Engineering at The University of Sheffield.
He has completed both his MEng in Mechanical Engineering and his PhD at Sheffield, the title of which was "Towards Bayesian System Identifcation: With Application to SHM of Offshore Structures".
His work focusses on application of Machine Learning and Bayesian statistical methods to problems in structural dynamics in particular those within nonlinear system identification and Structural Health Monitoring (SHM).
- Research interests
-
Current research interests include:
- Machine learning for structural dynamics and Structural Health Monitoring
- Bayesian statistical modelling of structural systems
- Probabilistic nonlinear system identification
- Joint input/state/parameter identification
- Publications
-
Journal articles
- Constraining Gaussian processes for physics-informed acoustic emission mapping. Mechanical Systems and Signal Processing, 188, 109984-109984.
- Resource-efficient machining through physics-informed machine learning. Procedia CIRP, 117, 347-352.
- On the dynamic properties of statistically-independent nonlinear normal modes. Mechanical Systems and Signal Processing, 181, 109510-109510.
- A latent restoring force approach to nonlinear system identification. Mechanical Systems and Signal Processing, 180, 109426-109426.
- Online damage detection of cutting tools using Dirichlet process mixture models. Mechanical Systems and Signal Processing, 180.
- A sampling-based approach for information-theoretic 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, 117063.
- A Bayesian methodology for localising acoustic emission sources in complex structures. Mechanical Systems and Signal Processing, 163, 108143-108143.
- Bayesian modelling of multivalued power curves from an operational wind farm. Mechanical Systems and Signal Processing, 108530-108530.
- Grey-box models for wave loading prediction. Mechanical Systems and Signal Processing, 159, 107741.
- Learning model discrepancy: A Gaussian process and sampling-based approach. Mechanical Systems and Signal Processing, 152, 107381-107381.
- Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(1), 03120003-03120003.
- Foundations of population-based SHM, Part I : homogeneous populations and forms. Mechanical Systems and Signal Processing, 148. View this article in WRRO
- Equation discovery for nonlinear dynamical systems : a Bayesian viewpoint. Mechanical Systems and Signal Processing, 154. View this article in WRRO
- Structured machine learning tools for modelling characteristics of guided waves. Mechanical Systems and Signal Processing, 156. View this article in WRRO
- Bayesian joint input-state estimation for nonlinear systems. Vibration, 3(3), 281-303.
- On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification. 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, 1124-1136. View this article in WRRO
- Probabilistic active learning : an online framework for structural health monitoring. Mechanical Systems and Signal Processing, 134. View this article in WRRO
- A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring. Mechanical Systems and Signal Processing, 119, 100-119. View this article in WRRO
- Sparse Gaussian Process Emulators for surrogate design modelling. Applied Mechanics and Materials, 885, 18-31. View this article in WRRO
- On evolutionary system identification with applications to nonlinear benchmarks. Mechanical Systems and Signal Processing, 112, 194-232. View this article in WRRO
- On the confidence bounds of Gaussian process NARX models and their higher-order frequency response functions. Mechanical Systems and Signal Processing, 104, 188-223. View this article in WRRO
- Physically meaningful uncertainty quantification in probabilistic wind turbine power curve models as a damage-sensitive feature. Structural Health Monitoring, 147592172311553-147592172311553.
- Distributions of fatigue damage from data-driven strain prediction using Gaussian process regression. Structural Health Monitoring, 147592172211400-147592172211400.
- A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves. Sensors, 23(1), 185-185.
- 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 Population-Based Structural Health Monitoring. Frontiers in Built Environment, 6.
- A Latent Restoring Force Approach to Nonlinear System Identification.
Chapters
- Physical Covariance Functions for Dynamic Systems with Time-Dependent Parameters, Lecture Notes in Civil Engineering (pp. 381-391). Springer International Publishing
- Bayesian Changepoint Modelling for Reference-Free Damage Detection with Acoustic Emission Data, Lecture Notes in Civil Engineering (pp. 462-471). Springer International Publishing
- Digital Transformation by the Implementation of the True Digital Twin Concept and Big Data Technology for Structural Integrity Management, Ageing and Life Extension of Offshore Facilities (pp. 143-157). ASME
- Structural Health Monitoring and Damage Identification, Handbook of Experimental Structural Dynamics (pp. 989-1061). Springer New York
- Physics-Informed Machine Learning for Structural Health Monitoring, Structural Integrity (pp. 347-367). Springer International Publishing
- Partially Supervised Learning for Data-Driven Structural Health Monitoring, Structural Integrity (pp. 389-411). Springer International Publishing
- Structural Health Monitoring and Damage Identification, Handbook of Experimental Structural Dynamics (pp. 1-72). Springer New York
- Heteroscedastic Gaussian Processes for Localising Acoustic Emission, Data Science in Engineering, Volume 9 (pp. 185-197).
- New Modes of Inference for Probabilistic SHM, Lecture Notes in Civil Engineering (pp. 415-426). Springer International Publishing
- Gaussian Process Based Grey-Box Modelling for SHM of Structures Under Fluctuating Environmental Conditions, Lecture Notes in Civil Engineering (pp. 55-66). Springer International Publishing
- Predicting Tool Wear Using Linear Response Surface Methodology and Gaussian Process Regression, Topics in Modal Analysis & Testing, Volume 8 (pp. 283-286). Springer International Publishing
- Bayesian Solutions to State-Space Structural Identification, Model Validation and Uncertainty Quantification, Volume 3 (pp. 247-253). Springer International Publishing
- Towards Population-Based Structural Health Monitoring, Part I: Homogeneous Populations and Forms, Model Validation and Uncertainty Quantification, Volume 3 (pp. 287-302). Springer International Publishing
- An Evolutionary Approach to Learning Neural Networks for Structural Health Monitoring, Model Validation and Uncertainty Quantification, Volume 3 (pp. 237-246). Springer International Publishing
- Structural Health Monitoring and Damage Identification, Handbook of Experimental Structural Dynamics (pp. 1-72). Springer New York
- Investigating Engineering Data by Probabilistic Measures, Special Topics in Structural Dynamics & Experimental Techniques, Volume 5 (pp. 77-81). Springer International Publishing
- State-of-the-Art and Future Directions for Predictive Modelling of Offshore Structure Dynamics Using Machine Learning, Conference Proceedings of the Society for Experimental Mechanics Series (pp. 223-233). Springer International Publishing
Conference proceedings papers
- Integrating Physical Knowledge into Gaussian Process Regression Models for Probabilistic Fatigue Assessment (pp 472-481)
- A Bayesian Approach to Lamb-Wave Dispersion Curve Material Identification in Composite Plates (pp 139-149)
- ON THE APPLICATION OF VARIATIONAL AUTO ENCODERS (VAE) FOR DAMAGE DETECTION IN ROLLING ELEMENT BEARINGS. Proceedings of the 13th International Workshop on Structural Health Monitoring View this article in WRRO
- OUTPUT-ONLY BAYESIAN SEMI-PARAMETRIC IDENTIFICATION OF A NONLINEAR DYNAMIC SYSTEM. 9th IOMAC International Operational Modal Analysis Conference, Proceedings (pp 156-164)
- ROBUST PROBABILISTIC CANONICAL CORRELATIONS FOR STOCHASTIC SUBSPACE IDENTIFICATION. 9th IOMAC International Operational Modal Analysis Conference, Proceedings (pp 124-132)
- Physics-derived covariance functions for machine learning in structural dynamics ⁎. IFAC-PapersOnLine, Vol. 54(7) (pp 168-173). Padova, Italy, 13 July 2021 - 16 July 2021.
- Decomposition of multi-mode signals using dispersion curves and Bayesian linear regression. Health Monitoring of Structural and Biological Systems XV, 22 March 2021 - 27 March 2021.
- Bayesian localisation of acoustic emission sources for wind turbine bearings. Health Monitoring of Structural and Biological Systems XV, 22 March 2021 - 27 March 2021.
- Physics-derived covariance functions for machine learning in structural dynamics. IFAC PAPERSONLINE, Vol. 54(7) (pp 168-173)
- Nonlinear Gaussian process latent force models for input estimation in hysteretic systems. UNCECOMP Proceedings, Vol. 2021-June
- Modelling of Guided Waves in a Composite Plate Through a Combination of Physical Knowledge and Regression Analysis (pp 109-114)
- 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 2891-2898)
- Data-driven strain prediction models and fatigue damage accumulation. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (pp 3667-3675)
- Constraining Gaussian processes for grey-box acoustic emission source localisation. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (pp 2789-2800)
- On the application of Gaussian process latent force models for Bayesian identification of the Duffing system. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (pp 2141-2153)
- On decision-making for adaptive models combining physics and data. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (pp 3623-3637)
- A spatiotemporal dual Kalman filter for the estimation of states and distributed inputs in dynamical systems. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (pp 3591-3597)
- A grey-box model for wave loading prediction with uncertainty propagation. Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics (pp 3611-3622)
- Identification of a Duffing oscillator using particle Gibbs with ancestor sampling. Journal of Physics: Conference Series, Vol. 1264. Valpre, Lyon, France, 15 April 2019 - 17 April 2019. View this article in WRRO
- 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
- Learning of model discrepancy for structural dynamics applications using Bayesian history matching. Journal of Physics : Conference Series, Vol. 1264(1), 15 April 2019 - 17 April 2019. View this article in WRRO
- A nonlinear robust outlier detection approach for SHM. 8th IOMAC - International Operational Modal Analysis Conference, Proceedings (pp 107-114) View this article in WRRO
- 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
- A semi-supervised bayesian non-parametric approach to damage detection. 9th European Workshop on Structural Health Monitoring, EWSHM 2018
- A Bayesian filtering approach to operational modal analysis with recovery of forcing signals. Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics (pp 5181-5194)
- On a Grey Box Modelling Framework for Nonlinear System Identification (pp 167-178)
- Identification of Nonlinear Wave Forces Using Gaussian Process NARX Models (pp 203-221)
- On the choice of optimisation scheme for Gaussian process hyperparameters in SHM problems. Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017, Vol. 2 (pp 2103-2111)
- On the Behaviour of Structures with Many Nonlinear Elements (pp 509-520)
- Output-only Identification of Loading and Modal Parameters in Dynamic Systems with Non-Gaussian Inputs. Structural Health Monitoring 2019, 24 October 2018 - 26 October 2018.
- Online Damage State Clustering of Cutting Tools Using Dirichlet Process Mixture Models. 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.
- Grey-box Modelling for Structural Health Monitoring: Physical Constraints on Machine Learning Algorithms. 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.
- Clustering-based Crack Growth Characterisation using Synchronised Vibration and Acoustic Emission Measurements. Structural Health Monitoring 2017, 12 September 2017 - 14 September 2017.
- A Gaussian Process Form for Population-Based Structural Health Monitoring. DAMAS 2019 View this article in WRRO
Preprints
- PAO: A general particle swarm algorithm with exact dynamics and closed-form transition densities, arXiv.
- A probabilistic approach for acoustic emission based monitoring techniques: with application to structural health monitoring, arXiv.
- A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves, arXiv.
- Physics-informed machine learning for Structural Health Monitoring, arXiv.
- Informative Bayesian Tools for Damage Localisation by Decomposition of
Lamb Wave Signals, arXiv.
- Bayesian Modelling of Multivalued Power Curves from an Operational Wind
Farm, arXiv.
- Grey-box models for wave loading prediction, arXiv.
- Probabilistic Inference for Structural Health Monitoring: New Modes of
Learning from Data, arXiv.
- Structured Machine Learning Tools for Modelling Characteristics of
Guided Waves, arXiv.
- A Bayesian Method for Material Identification of Composite Plates Via Dispersion Curves.
- Constraining Gaussian processes for physics-informed acoustic emission mapping. Mechanical Systems and Signal Processing, 188, 109984-109984.