Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's

Ken Bruton; Daniel Coakley; Peter O. Donovan; Marcus M. Keane; D. T.J. O'Sullivan

doi:10.1109/ETFA.2013.6647989

Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's

Ken Bruton
, Daniel Coakley
, Peter O. Donovan
, Marcus M. Keane
, D. T.J. O'Sullivan

University of Galway

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

Heating Ventilation and Air Conditioning (HVAC) system energy consumption, on average, accounts for 40% of an industrial site's total energy consumption. Studies have demonstrated that continuous commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHU's using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHU's across four commercial & industrial sites with over €104,000 annual energy savings detected by the AFDD tool.

Original language	English
Title of host publication	Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013
DOIs	https://doi.org/10.1109/ETFA.2013.6647989
Publication status	Published - 2013
Event	2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 - Cagliari, Italy Duration: 10 Sep 2013 → 13 Sep 2013

Publication series

Name	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
ISSN (Print)	1946-0740
ISSN (Electronic)	1946-0759

Conference

Conference	2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013
Country/Territory	Italy
City	Cagliari
Period	10/09/13 → 13/09/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/ETFA.2013.6647989

Cite this

Bruton, K., Coakley, D., Donovan, P. O., Keane, M. M., & O'Sullivan, D. T. J. (2013). Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's. In Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 Article 6647989 (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA). https://doi.org/10.1109/ETFA.2013.6647989

@inproceedings{f3745c69d80443068bdeea515d30ed45,

title = "Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's",

abstract = "Heating Ventilation and Air Conditioning (HVAC) system energy consumption, on average, accounts for 40\% of an industrial site's total energy consumption. Studies have demonstrated that continuous commissioning of building systems for optimum efficiency can yield savings of an average of over 20\% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHU's using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHU's across four commercial \& industrial sites with over €104,000 annual energy savings detected by the AFDD tool.",

author = "Ken Bruton and Daniel Coakley and Donovan, \{Peter O.\} and Keane, \{Marcus M.\} and O'Sullivan, \{D. T.J.\}",

year = "2013",

doi = "10.1109/ETFA.2013.6647989",

language = "English",

isbn = "9781479908622",

series = "IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",

booktitle = "Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013",

note = "2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 ; Conference date: 10-09-2013 Through 13-09-2013",

}

Bruton, K, Coakley, D, Donovan, PO, Keane, MM & O'Sullivan, DTJ 2013, Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's. in Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013., 6647989, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013, Cagliari, Italy, 10/09/13. https://doi.org/10.1109/ETFA.2013.6647989

Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's. / Bruton, Ken; Coakley, Daniel; Donovan, Peter O. et al.
Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013. 2013. 6647989 (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

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AU - O'Sullivan, D. T.J.

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N2 - Heating Ventilation and Air Conditioning (HVAC) system energy consumption, on average, accounts for 40% of an industrial site's total energy consumption. Studies have demonstrated that continuous commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHU's using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHU's across four commercial & industrial sites with over €104,000 annual energy savings detected by the AFDD tool.

AB - Heating Ventilation and Air Conditioning (HVAC) system energy consumption, on average, accounts for 40% of an industrial site's total energy consumption. Studies have demonstrated that continuous commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHU's using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHU's across four commercial & industrial sites with over €104,000 annual energy savings detected by the AFDD tool.

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SN - 9781479908622

T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

BT - Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013

T2 - 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013

Y2 - 10 September 2013 through 13 September 2013

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Bruton K, Coakley D, Donovan PO, Keane MM, O'Sullivan DTJ. Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's. In Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013. 2013. 6647989. (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA). doi: 10.1109/ETFA.2013.6647989

Results from testing of a 'cloud based' automated fault detection and diagnosis tool for AHU's

Abstract

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