Ken Bruton
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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
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
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
Review of automated fault detection and diagnostic tools in air handling units
K Bruton, P Raftery, B Kennedy, MM Keane, DTJ O’sullivan
Energy efficiency 7, 335-351, 2014
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
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
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
A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector
M Nacchia, F Fruggiero, A Lambiase, K Bruton
Applied Sciences 11 (6), 2546, 2021
Comparative analysis of the AHU InFO fault detection and diagnostic expert tool for AHUs with APAR
K Bruton, D Coakley, P Raftery, DO Cusack, MM Keane, DTJ O’sullivan
Energy Efficiency 8, 299-322, 2015
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
Progress in demand response and it’s industrial applications
SMS Siddiquee, B Howard, K Bruton, A Brem, DTJ O'Sullivan
Frontiers in Energy Research 9, 673176, 2021
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 (3), 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
IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities
P O'Donovan, K Bruton, DTJ O'Sullivan
PHM Society, 2016
Industrial smart and micro grid systems–A systematic mapping study
A Brem, MM Adrita, DTJ O’Sullivan, K Bruton
Journal of Cleaner Production 244, 118828, 2020
Data-driven quality improvement approach to reducing waste in manufacturing
R Clancy, D O'Sullivan, K Bruton
The TQM Journal 35 (1), 51-72, 2023
How do companies certified to ISO 50001 and ISO 14001 perform in LEED and BREEAM assessments?
A Brem, DÓ Cusack, MM Adrita, DTJ O’Sullivan, K Bruton
Energy efficiency 13, 751-766, 2020
A case-study in the introduction of a digital twin in a large-scale smart manufacturing facility
J O’Sullivan, D O’Sullivan, K Bruton
Procedia Manufacturing 51, 1523-1530, 2020
The true value of water: A case-study in manufacturing process water-management
BP Walsh, K Bruton, DTJ O'Sullivan
Journal of cleaner production 141, 551-567, 2017
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
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