Publications
2022
Chen, Yimin; Lin, Guanjing; Chen, Zhelun; Wen, Jin; Granderson, Jessica
A simulation-based evaluation of fan coil unit fault effects Journal Article
In: Energy and Buildings, vol. 263, pp. 112041, 2022, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: Fan Coil unit, Fault effects, Fault symptom evaluation, HVACSIM+, Symptom intensity, Symptom occurrence probability
@article{CHEN2022112041,
title = {A simulation-based evaluation of fan coil unit fault effects},
author = {Yimin Chen and Guanjing Lin and Zhelun Chen and Jin Wen and Jessica Granderson},
url = {https://www.sciencedirect.com/science/article/pii/S0378778822002122},
doi = {https://doi.org/10.1016/j.enbuild.2022.112041},
issn = {0378-7788},
year = {2022},
date = {2022-01-01},
journal = {Energy and Buildings},
volume = {263},
pages = {112041},
abstract = {Faults in heating, ventilation and air conditioning (HVAC) systems cause increased energy consumption, degrading thermal comforts, growing operational cost and reduced system lifespan. An effective evaluation of fault effects is critical to the development of various fault diagnostics solutions, the improvement of operation maintenance and the optimization of monitoring systems. In the HVAC area, a majority of research work in evaluating fault effects was to analyze energy consumption impacts or thermal comfort impacts. However, a handful of research has been conducted on evaluating fault effects on various measurements, which are increasingly employed to monitor equipment's operation. Fault effects on various measurements may display different symptom patterns and present changed sensitivities when the equipment operates under various faults, severity levels, as well as operation conditions. However, a long-term observation of fault symptoms under various operation conditions, different fault types and severity levels to evaluate fault effects is extremely challenging. In this paper, a simulation-based framework was proposed to evaluate fault effects in fan coil units (FCUs). Two metrics namely fault symptom occurrence probability (SOP) and fault symptom daily continuous duration (SDCD) were developed to quantify fault symptoms under various FCU faults. A total of 18 common FCU faults at different severity levels were implemented on the developed HVACSIM+ simulation platform to obtain a full year fault inclusive data set for 48 fault simulation cases. The framework, as well as obtained SOP and SDCD distributions will benefit multiple folds such as the development of probability-based fault diagnostics inference approaches, optimization of sensor location, and fault prioritization.},
keywords = {Fan Coil unit, Fault effects, Fault symptom evaluation, HVACSIM+, Symptom intensity, Symptom occurrence probability},
pubstate = {published},
tppubtype = {article}
}
Faults in heating, ventilation and air conditioning (HVAC) systems cause increased energy consumption, degrading thermal comforts, growing operational cost and reduced system lifespan. An effective evaluation of fault effects is critical to the development of various fault diagnostics solutions, the improvement of operation maintenance and the optimization of monitoring systems. In the HVAC area, a majority of research work in evaluating fault effects was to analyze energy consumption impacts or thermal comfort impacts. However, a handful of research has been conducted on evaluating fault effects on various measurements, which are increasingly employed to monitor equipment’s operation. Fault effects on various measurements may display different symptom patterns and present changed sensitivities when the equipment operates under various faults, severity levels, as well as operation conditions. However, a long-term observation of fault symptoms under various operation conditions, different fault types and severity levels to evaluate fault effects is extremely challenging. In this paper, a simulation-based framework was proposed to evaluate fault effects in fan coil units (FCUs). Two metrics namely fault symptom occurrence probability (SOP) and fault symptom daily continuous duration (SDCD) were developed to quantify fault symptoms under various FCU faults. A total of 18 common FCU faults at different severity levels were implemented on the developed HVACSIM+ simulation platform to obtain a full year fault inclusive data set for 48 fault simulation cases. The framework, as well as obtained SOP and SDCD distributions will benefit multiple folds such as the development of probability-based fault diagnostics inference approaches, optimization of sensor location, and fault prioritization.
2017
Pourarian, Shokouh; Wen, Jin; Veronica, Daniel; Pertzborn, Amanda; Zhou, Xiaohui; Liu, Ran
A tool for evaluating fault detection and diagnostic methods for fan coil units Journal Article
In: Energy and Buildings, vol. 136, pp. 151-160, 2017, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: AFDD, Fan Coil unit, Fault-free and faulty conditions, Simulation
@article{POURARIAN2017151,
title = {A tool for evaluating fault detection and diagnostic methods for fan coil units},
author = {Shokouh Pourarian and Jin Wen and Daniel Veronica and Amanda Pertzborn and Xiaohui Zhou and Ran Liu},
url = {https://www.sciencedirect.com/science/article/pii/S0378778816317698},
doi = {https://doi.org/10.1016/j.enbuild.2016.12.018},
issn = {0378-7788},
year = {2017},
date = {2017-01-01},
journal = {Energy and Buildings},
volume = {136},
pages = {151-160},
abstract = {Dynamic simulation tools that could accurately simulate operational data for both the fault-free and faulty dynamic operation of heating, ventilation, and air conditioning (HVAC) systems and equipment are needed for developing and evaluating advanced control and automated fault detection and diagnosis strategies. Among various HVAC subsystems, fan coil units (FCUs) are relatively simple, inexpensive devices that are used extensively in commercial, institutional and multifamily residential buildings. However, very little has been reported in the literature to improve FCU design and operation. There has also been a lack of dynamic simulation tool development focusing on FCUs. The work reported in this study aims at developing and validating a software tool to simulate operational data generated from FCUs that are operated dynamically under both faulty and fault-free conditions. A comprehensive and systematic validation process, using data collected from real FCUs in a laboratory building, is used to validate the tool under both faulty and fault-free operating conditions in different seasons. The validated tool not only is able to predict real-world FCU behaviors under different control strategies, but it is also able to predict symptoms associated with various faults, as well as the effects of those faults on system performance and occupant comfort.},
keywords = {AFDD, Fan Coil unit, Fault-free and faulty conditions, Simulation},
pubstate = {published},
tppubtype = {article}
}
Dynamic simulation tools that could accurately simulate operational data for both the fault-free and faulty dynamic operation of heating, ventilation, and air conditioning (HVAC) systems and equipment are needed for developing and evaluating advanced control and automated fault detection and diagnosis strategies. Among various HVAC subsystems, fan coil units (FCUs) are relatively simple, inexpensive devices that are used extensively in commercial, institutional and multifamily residential buildings. However, very little has been reported in the literature to improve FCU design and operation. There has also been a lack of dynamic simulation tool development focusing on FCUs. The work reported in this study aims at developing and validating a software tool to simulate operational data generated from FCUs that are operated dynamically under both faulty and fault-free conditions. A comprehensive and systematic validation process, using data collected from real FCUs in a laboratory building, is used to validate the tool under both faulty and fault-free operating conditions in different seasons. The validated tool not only is able to predict real-world FCU behaviors under different control strategies, but it is also able to predict symptoms associated with various faults, as well as the effects of those faults on system performance and occupant comfort.