| NSF Org: |
OIA OIA-Office of Integrative Activities |
| Recipient: |
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| Initial Amendment Date: | January 30, 2020 |
| Latest Amendment Date: | January 30, 2020 |
| Award Number: | 1929209 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Pinhas Ben-Tzvi
pbentzvi@nsf.gov (703)292-8246 OIA OIA-Office of Integrative Activities O/D Office Of The Director |
| Start Date: | February 1, 2020 |
| End Date: | January 31, 2023 (Estimated) |
| Total Intended Award Amount: | $217,143.00 |
| Total Awarded Amount to Date: | $217,143.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1000 E UNIVERSITY AVE LARAMIE WY US 82071-2000 (307)766-5320 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
610 Purdue Mall West Lafayette IN US 47907-2031 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | EPSCoR Research Infrastructure |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.083 |
ABSTRACT
Environmental impacts, as well as resource consumption, of building operations are significant throughout the entire life cycle of buildings. Heating ventilation and air conditioning (HVAC) systems consume about two-thirds of the total energy used in commercial buildings. Despite national efforts toward improving performance and sustainability, many existing HVAC systems in buildings do not run efficiently, due to equipment degradation, sensors being out of calibration, or improper control operations. Such problems can result in high maintenance costs, occupant discomfort, and wasted energy. Fault detection and diagnosis (FDD) for HVAC systems in buildings detect and identify operational faults based on the analysis of measured system behaviors. FDD technology is critical to improving building energy efficiency, and reducing or eliminating wasted energy in buildings caused by operational faults. The major challenge in current FDD technology is that the training data available to create diagnostic algorithms do not include all possible operating conditions that the testing systems experience throughout the life cycle. Given that the training data for FDDs does not cover all operating conditions, FDD algorithms for building HVAC systems must evolve along with the changes in building systems and components. The goal of this project is to enhance the robustness and efficiency of FDD technology for high-performance HVAC systems. The proposed research will lead to several broader impacts including research participation of underrepresented undergraduates, K-12 outreach activities, and sharing the experimental data and the FDD method for high-performance HVAC systems with other researchers. The knowledge gained from this research has the potential to significantly enhance building energy efficiency.
The overall research goal is to advance robustness and efficiency of Fault detection and diagnosis (FDD) technology through an adaptive machine learning-based approach for high-performance Heating ventilation and air conditioning (HVAC) systems. This research closes critical knowledge gaps in the FDDs for high-performance HVAC systems. First, the experimental study on common faults in high-performance HVAC systems at the Center for High Performance Buildings, Purdue University will result in a thorough understanding of fault features, including system behaviors as well as impacts on energy consumption and environmental conditions. While extensive research has been conducted on the FDD for conventional HVAC systems, the FDD for high-performance HVAC systems has rarely been studied. The experimental data pertaining to common faults in high-performance HVAC systems that will be obtained as a part of this project will, thus, be an invaluable asset to the FDD research community. Second, this research will yield an adaptive FDD method based on growing Gaussian mixture regressions for high-performance HVAC systems in commercial buildings. Traditional FDD methods learn from training data tested under limited operating conditions, after which the learning stops. This new FDD method adapts to the changes in HVAC operating environments, evolves with the changes in building systems and components, and learns to diagnose new faulty conditions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this project is to advance the robustness and efficiency of fault detection and diagnosis (FDD) technology by applying adaptive machine learning-based for high-performance heating, ventilation, and air conditioning (HVAC) systems. There are two research objectives: 1) Identify optimal sets of fault characteristic parameters for efficient and robust FDD in high-performance HVAC systems; and, 2) Create a new adaptive FDD method based on growing Gaussian mixture regressions (GGMR) for high-performance HVAC systems.
This study represents the first endeavor for detecting and diagnosing common faults of the high-performance HVAC systems. The evolving learning-based FDD method accurately detects and diagnoses existing faults and unknown faults. Specifically, in the new FDD method, the evolving learning algorithm GGMR is used to construct both a data-driven model representing normal performance and a transfer function for fault diagnosis.
We conducted experiments on common faults for high-performance HVAC systems in the Center for High-Performance Buildings at Purdue University. The two types of high-performance HVAC systems we investigated in this project are passive chilled beam and radiant slab systems. We first collected measurement data for normal operation and faulty operations for the two systems. The experiments on faulty operations include five types of faults at various severity levels for the passive chilled beam and the radiant slab systems. GGMR is used to predict energy demands for the passive chilled beam and the radiant slab systems under normal operations. GGMR is also used to construct transfer functions of fault diagnosis for the two high-performance HVAC systems. We then demonstrated the evolving learning-based FDD method for detecting and diagnosing common faults of the passive chilled beam and radiant slab systems.
Through this project, we also standardized the feature selection method and demonstrated the process using the two high-performance HVAC systems. To enhance the transferability of the FDD algorithm, we created a set of generalized performance indices for fault discrimination, including deviations between predictions (expectations) and measurements, relative differences between parameters, and features extracted from other performance indices such as cumulative differences or changes of parameters over time. Finding the best set of performance indices for detecting and diagnosing common faults in HVAC systems is defined as an optimization problem that maximizes the level of diagnosability with analytical redundancy taken into consideration.
This research closes critical knowledge gaps in FDD technology for high-performance HVAC systems. First, the experimental study on common faults for high-performance HVAC systems results in a thorough understanding of fault features, including system behaviors as well as impacts on energy consumption and environmental conditions. While extensive research has been conducted on FDDs for conventional HVAC systems, FDD for high-performance HVAC systems in commercial buildings has rarely been studied. Second, this research created an adaptive FDD method based on GGMR for high-performance HVAC systems in commercial buildings. Traditional FDDs learn from training data tested under limited operating conditions to detect and diagnose faults, after which they stop learning. This new FDD adapts to the changes in HVAC operating environments, evolves with the changes in building systems and components, and learns to diagnose new faulty conditions. The evolving learning-based FDD method as well as the standardized feature selection method can be applied to detecting and diagnosing common faults in other types of HVAC systems in buildings.
Last Modified: 06/06/2023
Modified by: Liping Wang
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