| 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), 1-26, 2015 | 241 | 2015 |
| Big data in manufacturing: a systematic mapping study P O'Donovan, K Leahy, K Bruton, DTJ O'Sullivan Journal of Big Data, 2015 | 196 | 2015 |
| A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications P O'Donovan, C Gallagher, K Bruton, DTJ O'Sullivan Manufacturing Letters, 2018 | 129 | 2018 |
| 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 | 72 | 2019 |
| Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units K Bruton, P Raftery, P O'Donovan, N Aughney, MM Keane, DTJ O'Sullivan Automation in Construction 39, 70-83, 2014 | 50 | 2014 |
| 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 | 34 | 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 | 32 | 2018 |
| 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 | 28 | 2017 |
| Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses K Leahy, C Gallagher, P O'Donovan, DTJ O'Sullivan Energies, 2019 | 21 | 2019 |
| Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing P O'Donovan, K Bruton, DTJ O'Sullivan International Journal of Prognostics and Health Management 7, 21, 2016 | 20 | 2016 |
| IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities P O'Donovan, K Bruton, DTJ O'Sullivan Internation Journal of Prognostics and Health Management 7, 11, 2016 | 18 | 2016 |
| A Systematic Analysis of Real-World Energy Blockchain Initiatives P O'Donovan, DTJ O'Sullivan Future Internet 11 (174), 2019 | 11 | 2019 |
| 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 | 10 | 2018 |
| A cloud-based distributed data collection system for decentralised wastewater treatment plants P O’Donovan, D Coburn, E Jones, L Hannon, M Glavin, D Mullins, ... Procedia Engineering 119, 464-469, 2015 | 9 | 2015 |
| 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 | 8 | 2019 |
| Waternomics: a cross-site data collection to support the development of a water information platform P O’Donovan, D Coakley, J Mink, E Curry, E Clifford Procedia Engineering 119, 458-463, 2015 | 6 | 2015 |
| Design and development of a software tool to assist ISO 50001 implementation in the manufacturing sector K Bruton, P O’Donovan, A McGregor, DDTJ O’Sullivan Proceedings of the Institution of Mechanical Engineers, Part B: Journal of …, 2018 | 5 | 2018 |
| Enabling effective operational decision making on a combined heat and power system using the 5C architecture K Bruton, BP Walsh, D óg Cusack, P O’Donovan, DTJ O'Sullivan Procedia CIRP 55, 296-301, 2016 | 5 | 2016 |
| Development of an Online Expert Rule based Automated Fault Detection and Diagnostic (AFDD) tool for Air Handling Units: Beta Test Results K Bruton, D Coakley, P O'Donovan, M Keane, D O'Sullivan Energy Systems Laboratory (http://esl. tamu. edu), 2013 | 5 | 2013 |
| 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 | 4 | 2018 |
Kevin LeahyPhD Researcher, IERG, UCCVerified email at umail.ucc.ie
