Publications
2015
Zhao, Yang; Wen, Jin; Wang, Shengwei
Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors Journal Article
In: Applied Thermal Engineering, vol. 90, pp. 145-157, 2015, ISSN: 1359-4311.
Abstract | Links | BibTeX | Tags: Air handling unit, Bayesian network, Fault detection, Fault diagnosis
@article{ZHAO2015145,
title = {Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors},
author = {Yang Zhao and Jin Wen and Shengwei Wang},
url = {https://www.sciencedirect.com/science/article/pii/S1359431115006584},
doi = {https://doi.org/10.1016/j.applthermaleng.2015.07.001},
issn = {1359-4311},
year = {2015},
date = {2015-01-01},
journal = {Applied Thermal Engineering},
volume = {90},
pages = {145-157},
abstract = {This is the second part of a study on diagnostic Bayesian networks (DBNs)-based method for diagnosing faults in air handling units (AHUs) in buildings. In this part, 4 DBNs are developed to diagnose faults in heating/cooling coils, sensors and faults in secondary supply chilled water/heating water systems. There are 18 typical faults concerned and 35 fault detectors introduced. The DBNs are developed mainly on the basis of first principles and fault patterns resulted from literature and three AHU fault detection and diagnosis (FDD) projects. Efficient fault detection rules/methods from a comprehensive literature survey are integrated into the DBNs. Also, some new fault detection rules are developed. The 4 DBNs were evaluated using experimental data from ASHRAE Project RP-1312. Results show that the proposed DBNs effectively diagnosed AHU faults.},
keywords = {Air handling unit, Bayesian network, Fault detection, Fault diagnosis},
pubstate = {published},
tppubtype = {article}
}
This is the second part of a study on diagnostic Bayesian networks (DBNs)-based method for diagnosing faults in air handling units (AHUs) in buildings. In this part, 4 DBNs are developed to diagnose faults in heating/cooling coils, sensors and faults in secondary supply chilled water/heating water systems. There are 18 typical faults concerned and 35 fault detectors introduced. The DBNs are developed mainly on the basis of first principles and fault patterns resulted from literature and three AHU fault detection and diagnosis (FDD) projects. Efficient fault detection rules/methods from a comprehensive literature survey are integrated into the DBNs. Also, some new fault detection rules are developed. The 4 DBNs were evaluated using experimental data from ASHRAE Project RP-1312. Results show that the proposed DBNs effectively diagnosed AHU faults.