Fault detection in commercial building VAV AHU: A case study of an academic building

Deshmukh, Suhrid; Samouhos, Stephen; Glicksman, Leon R.; Norford, Leslie K.

doi:10.1016/j.enbuild.2019.06.051

Cited by 42 publications

(16 citation statements)

References 19 publications

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“…Accordingly, the equation for the relationship between the damper opening ratio and differential pressure ratio was derived by using the measured data at the maximum fan speed, as shown in Figure 8. The relationship between the damper opening ratio and the differential pressure ratio can be represented by a cubic equation, as shown in Equation (17). β = 1.4375α 3 − 2.2664α 2 + 0.3041α + 0.9889 (17) Energies 2019, 12, x FOR PEER REVIEW 9 of 14 Figure 6.…”

Section: Methodsmentioning

confidence: 99%

“…The relationship between the damper opening ratio and the differential pressure ratio can be represented by a cubic equation, as shown in Equation (17). β = 1.4375α 3 − 2.2664α 2 + 0.3041α + 0.9889 (17) Energies 2019, 12, x FOR PEER REVIEW 9 of 14 Figure 6. Generation of in-situ damper performance curve at full speed.…”

Section: Methodsmentioning

confidence: 99%

“…Precise measurement of the minimum air flow is essential for energy saving through advanced VAV system control, such as DCV (demand controlled ventilation) [13], OBC (occupancy-based control) [14], and temperature setpoint reset [15]. It can also be used to fault the detection and diagnostic of air flow sensors and air flow controls in VAV systems [16][17][18]. In recent research, the virtual sensor is a method for estimating a new physical quantity using existing physical quantities [19], and has been developed and applied in many parts (water flow rate of pump [20,21], mixing air temperature [22], etc.)…”

Section: Typical Airflow Measurement Of the Vav Terminal Unitmentioning

confidence: 99%

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Development of Virtual Air Flow Sensor Using In-Situ Damper Performance Curve in VAV Terminal Unit

Kim

Cho

2019

Energies

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In this study, we developed a virtual air flow sensor using an in-situ damper performance curve to secure the stability of control of the variable air volume (VAV) terminal unit, and also established the in-situ measurement procedure. The minimum air flow rate of the VAV terminal unit was related to the energy consumption, it was important to determine the minimum air flow rate suitable for the situation of each room in terms of energy saving. However, it was difficult to set the minimum air flow rate low setpoint due to the low accuracy of the air flow sensor of the VAV terminal unit. This paper suggested a virtual air flow sensing method using an in-situ damper performance curve in the VAV terminal unit. The input factors of the virtual sensor were developed on the basis of the supply fan speed and damper opening ratio, which could be easily obtained from the existing control system. In addition, the in-situ measurement procedure of the virtual air flow sensor was developed by dividing the procedure into five steps. Finally, reliability of the virtual air flow sensor was evaluated through uncertainty analysis of input variables and relative error analysis, in comparison with the conventional air flow rate measurement method. The developed virtual air flow sensor was found to have an uncertainty of up to 8.8%, and it was also found that the closer to the maximum the values of the input variables, the lower the uncertainty. In addition, verification of relative error with respect to the measured values by the hot-wire anemometer was conducted by varying operation conditions to a total of 12 cases, and as a result, relative error was found to be up to 5.6%. In addition, the results of long-term experiment showed that relative error was within about 9.5%, and thus, the feasibility for field application and control was confirmed.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Typical Airflow Measurement Of the Vav Terminal Unitmentioning

confidence: 99%

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Development of Virtual Air Flow Sensor Using In-Situ Damper Performance Curve in VAV Terminal Unit

Kim

Cho

2019

Energies

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“…After the statistical analysis and data cleaning processes, the variables are correlated using the classical Pearson's correlation coefficient, which quantifies the linear distance between two variables [35]. This provides an approximate view of the dependency level between each pair of variables.…”

Section: Correlation Analysismentioning

confidence: 99%

“…(a) The approach is based on the autonomous paradigm for the self-supervision process; (b) The approach considers the integration of several machine learning approaches for the supervision; (c) The approach is easily adaptable and extensible; (d) The approach considers different aspects for a correct supervision: detection, diagnosis, among others. [2] x x [3] x [23] x x x [26] x [32,35] x x Our work…”

Section: Comparison With Other Workmentioning

confidence: 99%

An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Aguilar¹,

Ardila²,

Avendaño³

et al. 2020

Energies

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Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.

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Faults’ Effects in Air-Handling Units: A Comprehensive Analysis of Numerical Studies

Rosato,

Mercuri,

Guarino

et al. 2024

Smart Innovation, Systems and Technologies

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Fault detection in commercial building VAV AHU: A case study of an academic building

Cited by 42 publications

References 19 publications

Development of Virtual Air Flow Sensor Using In-Situ Damper Performance Curve in VAV Terminal Unit

Development of Virtual Air Flow Sensor Using In-Situ Damper Performance Curve in VAV Terminal Unit

An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Faults’ Effects in Air-Handling Units: A Comprehensive Analysis of Numerical Studies

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