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Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings./
Author:	Ebrahimifakhar, Amir.
Description:	1 online resource (127 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Architectural engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28865338click for full text (PQDT)
ISBN:	9798759958406

Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings.
Ebrahimifakhar, Amir.

Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings. - 1 online resource (127 pages)

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2021.

Includes bibliographical references

This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider's dataset, to convert the fault reports to a single standardized fault identifier. The fault identifier is taken from a standard taxonomy that was created for this purpose.Since the commercial FDD software outputs are inherently subject to some level of error, i.e., they could have false negatives and false positives, a field study was conducted to gain greater insight into the commercial FDD software results. Two buildings from among the buildings of one of the FDD providers were selected. The RTUs serving these two buildings were monitored for about two weeks using our installed data loggers. The actual faults in these buildings were identified using methods that we developed or selected from the literature. The results of the field study were compared with the FDD provider fault reports.This study also proposes a data-driven FDD strategy for RTUs, using machine learning classification methods. The FDD task is formulated as a multi-class classification problem. Seven typical RTU faults are discriminated against one another as well as the normal condition. Nine classification methods were applied to a dataset of simulation data, which was split into a training set and a test set. The performance of the classifiers for individual faults was characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables was analyzed, and is also discussed in the dissertation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798759958406Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Commercial buildingsIndex Terms--Genre/Form:

542853
Electronic books.

Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings.
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520 $a This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider's dataset, to convert the fault reports to a single standardized fault identifier. The fault identifier is taken from a standard taxonomy that was created for this purpose.Since the commercial FDD software outputs are inherently subject to some level of error, i.e., they could have false negatives and false positives, a field study was conducted to gain greater insight into the commercial FDD software results. Two buildings from among the buildings of one of the FDD providers were selected. The RTUs serving these two buildings were monitored for about two weeks using our installed data loggers. The actual faults in these buildings were identified using methods that we developed or selected from the literature. The results of the field study were compared with the FDD provider fault reports.This study also proposes a data-driven FDD strategy for RTUs, using machine learning classification methods. The FDD task is formulated as a multi-class classification problem. Seven typical RTU faults are discriminated against one another as well as the normal condition. Nine classification methods were applied to a dataset of simulation data, which was split into a training set and a test set. The performance of the classifiers for individual faults was characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables was analyzed, and is also discussed in the dissertation.
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