| Machine learning pipeline for battery state-of-health estimation D Roman, S Saxena, V Robu, M Pecht, D Flynn Nature Machine Intelligence 3 (5), 447-456, 2021 | 441 | 2021 |
| Prognostics and health management for the optimization of marine hybrid energy systems W Tang, D Roman, R Dickie, V Robu, D Flynn Energies 13 (18), 4676, 2020 | 44 | 2020 |
| AI-driven maintenance support for downhole tools and electronics operated in dynamic drilling environments L Kirschbaum, D Roman, G Singh, J Bruns, V Robu, D Flynn IEEE Access 8, 78683-78701, 2020 | 42 | 2020 |
| Battery stress factor ranking for accelerated degradation test planning using machine learning S Saxena, D Roman, V Robu, D Flynn, M Pecht energies 14 (3), 723, 2021 | 32 | 2021 |
| A machine learning degradation model for electrochemical capacitors operated at high temperature D Roman, S Saxena, J Bruns, R Valentin, M Pecht, D Flynn IEEE Access 9, 25544-25553, 2021 | 22 | 2021 |
| A review of the role of prognostics in predicting the remaining useful life of assets D Roman, R Dickie, V Robu, D Flynn Safety and Reliability-Theory and Applications 135, 2017 | 17 | 2017 |
| Deep learning pipeline for state-of-health classification of electromagnetic relays L Kirschbaum, D Roman, V Robu, D Flynn 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), 1-7, 2021 | 4 | 2021 |
| Machine Learning Pipeline for Power Electronics State of Health Assessment and Remaining Useful Life Prediction CL Kahraman, D Roman, L Kirschbaum, D Flynn, J Swingler IEEE Access, 2024 | | 2024 |
| Design and implementation of machine learning algorithms for degradation estimation of Lithium-ion batteries and electrochemical capacitors D Roman Heriot-Watt University, 2021 | | 2021 |
David FlynnProfessor , University of GlasgowVerified email at Glasgow.ac.uk