| An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities P O’Donovan, K Leahy, K Bruton, DTJ O’Sullivan Journal of big data 2, 1-26, 2015 | 357 | 2015 |
| Big data in manufacturing: a systematic mapping study P O’donovan, K Leahy, K Bruton, DTJ O’Sullivan Journal of Big Data 2, 1-22, 2015 | 260 | 2015 |
| Diagnosing wind turbine faults using machine learning techniques applied to operational data K Leahy, RL Hu, IC Konstantakopoulos, CJ Spanos, AM Agogino 2016 ieee international conference on prognostics and health management …, 2016 | 137 | 2016 |
| A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications P O’Donovan, C Gallagher, K Leahy, DTJ O’Sullivan Computers in industry 110, 12-35, 2019 | 122 | 2019 |
| Diagnosing and predictingwind turbine faults from scada data using support vector machines K Leahy, RL Hu, IC Konstantakopoulos, CJ Spanos, AM Agogino, ... International Journal of Prognostics and Health Management 9 (1), 2018 | 83 | 2018 |
| The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings CV Gallagher, K Bruton, K Leahy, DTJ O’Sullivan Energy and Buildings 158, 647-655, 2018 | 61 | 2018 |
| A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study K Leahy, C Gallagher, P O’Donovan, K Bruton, DTJ O’Sullivan Energies 11 (7), 1738, 2018 | 59 | 2018 |
| Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0 CV Gallagher, K Leahy, P O’Donovan, K Bruton, DTJ O’Sullivan Energy and Buildings 167, 8-22, 2018 | 56 | 2018 |
| Issues with data quality for wind turbine condition monitoring and reliability analyses K Leahy, C Gallagher, P O’Donovan, DTJ O’Sullivan Energies 12 (2), 201, 2019 | 48 | 2019 |
| Using domain knowledge features for wind turbine diagnostics RL Hu, K Leahy, IC Konstantakopoulos, DM Auslander, CJ Spanos, ... 2016 15th IEEE International Conference on Machine Learning and Applications …, 2016 | 39 | 2016 |
| Automatically identifying and predicting unplanned wind turbine stoppages using scada and alarms system data: Case study and results K Leahy, C Gallagher, K Bruton, P O’Donovan, DTJ O’Sullivan Journal of Physics: Conference Series 926 (1), 012011, 2017 | 37 | 2017 |
| Cluster analysis of wind turbine alarms for characterising and classifying stoppages K Leahy, C Gallagher, P O'Donovan, DTJ O'Sullivan IET Renewable Power Generation 12 (10), 1146-1154, 2018 | 19 | 2018 |
| IntelliMaV: A cloud computing measurement and verification 2.0 application for automated, near real-time energy savings quantification and performance deviation detection CV Gallagher, K Leahy, P O’Donovan, K Bruton, DTJ O’Sullivan Energy and buildings 185, 26-38, 2019 | 17 | 2019 |
| A data pipeline for PHM data-driven analytics in large-scale smart manufacturing facilities PO Donovan, K Leahy, DO Cusack, K Bruton, DTJ O’Sullivan Annual conference of the prognostics and health management society 6, 1-10, 2015 | 4 | 2015 |
| From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings CV Gallagher, P O’Donovan, K Leahy, K Bruton, DTJ O’Sullivan 2018 International Conference on Smart Energy Systems and Technologies (SEST …, 2018 | 3 | 2018 |
| Manipulation of fermentation dynamics and its effect on silage production, rumen fermentation and animal performance KT Leahy | 2 | 1988 |
| Industrial Big Data Pipeline for Wind Turbine PHM in a Large Manufacturing Facility K Leahy, C Gallagher, P O’Donovan, DTJ O’Sullivan International Journal of Prognostics and Health Management 10 (1), 1-4, 2019 | 1 | 2019 |
| Data analytics for fault prediction and diagnosis in wind turbines K Leahy University College Cork, 2018 | 1 | 2018 |
| Implementing the Green Batch: A case study: Continuous statistical evaluation to achieve the most energy efficient and reliable process K Leahy, K Bruton, D O'Sullivan Proceedings of the 2014 IEEE Emerging Technology and Factory Automation …, 2014 | 1 | 2014 |
| Cluster analysis of wind turbine alarms for characterising and classifying K Leahy, C Gallagher, P O'Donovan, D O'Sullivan | | 2018 |
Dominic O'SullivanLecturer, University College CorkDirección de correo verificada de ucc.ie
