Estimating airflow rates in air-handling units from actuator control signals

Tan, Huiling; Dexter, A.L.

doi:10.1016/j.buildenv.2005.05.027

Cited by 15 publications

(4 citation statements)

References 8 publications

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“…Given values for P0, Qr/Qs, Re(x), Ri(x), and Rx(x), these equations can be solved for the variables P1, P2, Qe, Qx/Qs, and Qi/Qs . Doing this, one finds for the variables of interest (Tan 2006):…”

Section: Fault Detection Based On a Simple Mixing Box Modelmentioning

confidence: 99%

“…Assuming Ri,open = Re,open = Rx,open, it can be easily shown that Ri, Re, and Rx in the equations for a(x), b(x), and c(x) can be replaced by Ri′, Re′, and Rx′, where (Tan 2006): Figure 3 shows the results of this mixing box model for values of kd = 7.578 (Legg 1986), Ai = 0.1, Ae = 0.5, and Ax = 0.2 (Tan 2006), and assuming a balanced system, i.e., Qr = Qs. For values of Qr/Qs < 1, which corresponds to pressurizing the building, one finds that for small values of x it is possible to have the flow through the exhaust air damper reversed with some outside air coming in through this damper.…”

Section: Fault Detection Based On a Simple Mixing Box Modelmentioning

confidence: 99%

“…The paper examines which of the above faults can be detected and under what conditions. A simple mixing box model (Tan 2006) and a simple cooling coil model (Wang 2004) are used to represent the performance of a typical mixing box and cooling coil in a VAV AHU. Since the purpose of this work was to explore how intelligent agents could be used for fault detection in real HVAC applications, system identification is performed using results from the simple mixing box and cooling coil models.…”

Section: Introductionmentioning

confidence: 99%

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Detecting faults in building air handling units – a background study for developing intelligent agents for optimizing building HVAC systems

Kelly¹,

Bushby²

2014

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One approach to the optimization of building heating, ventilation and airconditioning (HVAC) systems involves the use of intelligent agents. These Agents can be used for minimizing energy consumption, maximizing occupant comfort, fault detection, performing diagnostics, and improving system commissioning. This paper lays the ground work for using intelligent agents for fault detection in HVAC applications involving air handling units (AHUs). Specifically, it looks at which faults in AHU mixing boxes and cooling coils can be detected and under what conditions. Such an understanding is necessary before intelligent agents can be developed, tested, and implemented in real building HVAC systems. A simple mixing box model (Tan 2006) and a simple cooling coil model (Wang 2004) are used to represent the performance of a typical AHU, and system identification is performed using piece-wise linear approximations. Questions addressed include which variables should be used in the identification process, how many linear segments should be used for each variable, how large a fault must be in order to be detected given the inherent inaccuracies in the piece-wise linear approximation process, and over what range of external variables (e.g., environmental conditions) is fault detection possible.

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Section: Fault Detection Based On a Simple Mixing Box Modelmentioning

confidence: 99%

Section: Fault Detection Based On a Simple Mixing Box Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting faults in building air handling units – a background study for developing intelligent agents for optimizing building HVAC systems

Kelly¹,

Bushby²

2014

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“…The air-circuits of a Variable-air-Volume (VAV) airconditioning system have been simulated to investigate the relationship between the airflow rates and actuator control signals (Tan and Dexter, 2004).…”

Section: Estimation Of Airflow Rates Using Actuator Control Signalsmentioning

confidence: 99%

Improving the Accuracy of Sensors in Air-Conditioning Systems

Tan

Dexter

2005

IFAC Proceedings Volumes

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Improving the accuracy of the measurements in building automation systems without increasing operational costs is an essential step if the overall comfort and energy efficiency of buildings are to be improved. Data reconciliation and gross error elimination have emerged as key techniques for reducing both random noise and gross errors on the outputs of sensors. This paper focuses on using actuator control signals and simulation based on computational fluid dynamics to achieve the level of redundancy that is necessary for simultaneous data reconciliation and gross error elimination to be applied air-conditioning systems.

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Characteristics of Information‐Poor Systems

Dexter

2012

Monitoring and Control of Information‐Poor Systems

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Estimating airflow rates in air-handling units from actuator control signals

Cited by 15 publications

References 8 publications

Detecting faults in building air handling units – a background study for developing intelligent agents for optimizing building HVAC systems

Detecting faults in building air handling units – a background study for developing intelligent agents for optimizing building HVAC systems

Improving the Accuracy of Sensors in Air-Conditioning Systems

Characteristics of Information‐Poor Systems

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