Publications
2022
Chen, Yimin; Wen, Jin; Pradhan, Ojas; Lo, L. James; Wu, Teresa
Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems Journal Article
In: Applied Energy, vol. 327, pp. 120050, 2022, ISSN: 0306-2619.
Abstract | Links | BibTeX | Tags: Cross-level fault, Discrete Bayesian Network, HVAC system, Pattern Matching, Root cause fault diagnosis
@article{CHEN2022120050,
title = {Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems},
author = {Yimin Chen and Jin Wen and Ojas Pradhan and L. James Lo and Teresa Wu},
url = {https://www.sciencedirect.com/science/article/pii/S0306261922013071},
doi = {https://doi.org/10.1016/j.apenergy.2022.120050},
issn = {0306-2619},
year = {2022},
date = {2022-01-01},
journal = {Applied Energy},
volume = {327},
pages = {120050},
abstract = {Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.},
keywords = {Cross-level fault, Discrete Bayesian Network, HVAC system, Pattern Matching, Root cause fault diagnosis},
pubstate = {published},
tppubtype = {article}
}
Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.
2007
Wen, Jin; Smith, Theodore F.
Development and validation of online models with parameter estimation for a building zone with VAV system Journal Article
In: Energy and Buildings, vol. 39, no. 1, pp. 13-22, 2007, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: HVAC system, Online modeling, Parameter estimation, Recursive least square method, Variable air volume system
@article{WEN200713,
title = {Development and validation of online models with parameter estimation for a building zone with VAV system},
author = {Jin Wen and Theodore F. Smith},
url = {https://www.sciencedirect.com/science/article/pii/S037877880600137X},
doi = {https://doi.org/10.1016/j.enbuild.2006.04.016},
issn = {0378-7788},
year = {2007},
date = {2007-01-01},
journal = {Energy and Buildings},
volume = {39},
number = {1},
pages = {13-22},
abstract = {The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked increasing attention to promote energy efficient control and operation of HVAC systems. Application of advanced control and operation strategies requires robust online system models. In this study, online models with parameter estimation for a building zone with a variable air volume system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and zone entering air flow are modeled based on physical rules and only the measurements that are commonly available in a commercial building are used. Various validation experiments were performed using a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all validation experiments are less than 0.28°C for temperature outputs, and less than 84.9m3/h for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.},
keywords = {HVAC system, Online modeling, Parameter estimation, Recursive least square method, Variable air volume system},
pubstate = {published},
tppubtype = {article}
}
The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked increasing attention to promote energy efficient control and operation of HVAC systems. Application of advanced control and operation strategies requires robust online system models. In this study, online models with parameter estimation for a building zone with a variable air volume system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and zone entering air flow are modeled based on physical rules and only the measurements that are commonly available in a commercial building are used. Various validation experiments were performed using a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all validation experiments are less than 0.28°C for temperature outputs, and less than 84.9m3/h for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.