Publications
2021
Lu, Xing; Fu, Yangyang; O’Neill, Zheng; Wen, Jin
In: Energy and Buildings, vol. 252, pp. 111448, 2021, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: ASHRAE guideline 36, Fault simulation, High-performance control sequences, HVAC, Modelica
@article{LU2021111448,
title = {A holistic fault impact analysis of the high-performance sequences of operation for HVAC systems: Modelica-based case study in a medium-office building},
author = {Xing Lu and Yangyang Fu and Zheng O'Neill and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0378778821007325},
doi = {https://doi.org/10.1016/j.enbuild.2021.111448},
issn = {0378-7788},
year = {2021},
date = {2021-01-01},
journal = {Energy and Buildings},
volume = {252},
pages = {111448},
abstract = {ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating, Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30% energy under ideal simulation environments. However, HVAC systems are susceptible to various types of faults in a real building operation. There are no existing studies that pertain to a comprehensive fault impact analysis of the high-performance control sequences suggested by ASHRAE Guideline 36 for HVAC systems. How these sequences handle and adapt to the various types of faults is still largely unknown. In this context, a comprehensive fault impact analysis and robustness assessment of the high-performance control sequences is conducted. A Modelica-based medium office virtual testbed is developed following the air-side and the plant-side SOO. A total of 359 fault scenarios in three different seasonal operating conditions (cooling, shoulder, and heating seasons) are injected into the baseline model. The evaluated key performance indexes (KPIs) include the operational cost, source energy, site energy, control loop quality, thermal comfort, ventilation, and power system metrics. The faults of the most negative impact are identified for different seasonal operating conditions over all the KPIs. The results also show that high-performance control sequences are well adapted for the vast majority (∼90%) of all the fault scenarios over all the KPIs in this study.},
keywords = {ASHRAE guideline 36, Fault simulation, High-performance control sequences, HVAC, Modelica},
pubstate = {published},
tppubtype = {article}
}
ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating, Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30% energy under ideal simulation environments. However, HVAC systems are susceptible to various types of faults in a real building operation. There are no existing studies that pertain to a comprehensive fault impact analysis of the high-performance control sequences suggested by ASHRAE Guideline 36 for HVAC systems. How these sequences handle and adapt to the various types of faults is still largely unknown. In this context, a comprehensive fault impact analysis and robustness assessment of the high-performance control sequences is conducted. A Modelica-based medium office virtual testbed is developed following the air-side and the plant-side SOO. A total of 359 fault scenarios in three different seasonal operating conditions (cooling, shoulder, and heating seasons) are injected into the baseline model. The evaluated key performance indexes (KPIs) include the operational cost, source energy, site energy, control loop quality, thermal comfort, ventilation, and power system metrics. The faults of the most negative impact are identified for different seasonal operating conditions over all the KPIs. The results also show that high-performance control sequences are well adapted for the vast majority (∼90%) of all the fault scenarios over all the KPIs in this study.
2014
Li, Shun; Wen, Jin
A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform Journal Article
In: Energy and Buildings, vol. 68, pp. 63-71, 2014, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: AHU, BAS, Fault detection and diagnostics, HVAC, Principle Component Analysis, Wavelet transform
@article{LI201463,
title = {A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform},
author = {Shun Li and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0378778813005410},
doi = {https://doi.org/10.1016/j.enbuild.2013.08.044},
issn = {0378-7788},
year = {2014},
date = {2014-01-01},
journal = {Energy and Buildings},
volume = {68},
pages = {63-71},
abstract = {Building automation systems (BASs) are widely used in modern buildings and large amounts of data are available on the BAS central station. This abundance of data has been described as a data rich but information poor situation and has given an opportunity to better utilize the collected BAS data for fault detection and diagnostics (AFDD) purposes. Air-handling units (AHUs) operate in dynamic environment with changing weather conditions and internal loads. It is challenging for FDD method to distinguish differences caused by normal weather conditions change or by faults. Principle Component Analysis (PCA) has been found to be powerful as a data-driven model based method in detecting AHU faults. Wavelet transform is a promising data preprocess approach to solve the problem by removing the influence of weather condition change. A combined Wavelet-PCA method is developed and tested using site-data. The feasibility of using wavelet transform method for data pretreatment has been demonstrated in this study. Comparing to conventional PCA method, Wavelet-PCA method is more robust to the internal load change and weather impact and generate no false alarms.},
keywords = {AHU, BAS, Fault detection and diagnostics, HVAC, Principle Component Analysis, Wavelet transform},
pubstate = {published},
tppubtype = {article}
}
Building automation systems (BASs) are widely used in modern buildings and large amounts of data are available on the BAS central station. This abundance of data has been described as a data rich but information poor situation and has given an opportunity to better utilize the collected BAS data for fault detection and diagnostics (AFDD) purposes. Air-handling units (AHUs) operate in dynamic environment with changing weather conditions and internal loads. It is challenging for FDD method to distinguish differences caused by normal weather conditions change or by faults. Principle Component Analysis (PCA) has been found to be powerful as a data-driven model based method in detecting AHU faults. Wavelet transform is a promising data preprocess approach to solve the problem by removing the influence of weather condition change. A combined Wavelet-PCA method is developed and tested using site-data. The feasibility of using wavelet transform method for data pretreatment has been demonstrated in this study. Comparing to conventional PCA method, Wavelet-PCA method is more robust to the internal load change and weather impact and generate no false alarms.
Li, Shun; Wen, Jin
Application of pattern matching method for detecting faults in air handling unit system Journal Article
In: Automation in Construction, vol. 43, pp. 49-58, 2014, ISSN: 0926-5805.
Abstract | Links | BibTeX | Tags: AHU, BAS, Fault detection and diagnostics, HVAC, Pattern Matching, Principle Component Analysis
@article{LI201449,
title = {Application of pattern matching method for detecting faults in air handling unit system},
author = {Shun Li and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0926580514000545},
doi = {https://doi.org/10.1016/j.autcon.2014.03.002},
issn = {0926-5805},
year = {2014},
date = {2014-01-01},
journal = {Automation in Construction},
volume = {43},
pages = {49-58},
abstract = {This paper presents a hybrid air handling unit (AHU) fault detection strategy based on Principal Component Analysis (PCA) method and Pattern Matching method. The basic idea of the pattern matching method is to locate periods of operation from a historical data set whose operational conditions are similar to the target operating condition. The proposed Pattern Matching-PCA method uses two similarity factors, PCA similarity factors and Distance similarity factors, to characterize the degree of similarity between historical data window and current snapshot data. PCA model is then built using the historical AHU operation dataset that are identified to be similar to current snapshot operation data. The method is validated by operational data of an AHU system in real building. The results show that the sensibility of PCA models is enhanced by preprocessing the training data with the Pattern Matching method.},
keywords = {AHU, BAS, Fault detection and diagnostics, HVAC, Pattern Matching, Principle Component Analysis},
pubstate = {published},
tppubtype = {article}
}
This paper presents a hybrid air handling unit (AHU) fault detection strategy based on Principal Component Analysis (PCA) method and Pattern Matching method. The basic idea of the pattern matching method is to locate periods of operation from a historical data set whose operational conditions are similar to the target operating condition. The proposed Pattern Matching-PCA method uses two similarity factors, PCA similarity factors and Distance similarity factors, to characterize the degree of similarity between historical data window and current snapshot data. PCA model is then built using the historical AHU operation dataset that are identified to be similar to current snapshot operation data. The method is validated by operational data of an AHU system in real building. The results show that the sensibility of PCA models is enhanced by preprocessing the training data with the Pattern Matching method.