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Colm V. Gallagher
Colm V. Gallagher
Eaton - Centre for Intelligent Power
Verified email at eaton.com
Title
Cited by
Cited by
Year
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 15, 139-142, 2018
1312018
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
742019
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
392018
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
352018
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
322018
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
282017
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
222019
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
112018
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
92019
Utilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savings
CV Gallagher, K Bruton, DTJ O’Sullivan
International Conference on Data Mining and Big Data, 48-58, 2016
52016
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
42018
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), 2019
2019
A data science solution for measurement and verification 2.0 in industrial buildings
CV Gallagher
University College Cork, 2019
2019
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Articles 1–13