Air Handling Unit Level Fault Signature Development Using EnergyPlus

2012 ◽  
Author(s):  
Li Song ◽  
Briana Branesky
Keyword(s):  
Energy Consumption ◽  
Fault Detection ◽  
Energy Loss ◽  
Fault Detection And Diagnosis ◽  
Base Case ◽  
Unit Level ◽  
University Of Oklahoma ◽  
Air Handling Unit ◽  
Damper Control ◽  
Faulty Component

In the effort to create more energy efficient buildings, an effective fault detection process must be developed for monitoring and diagnosing malfunctions in the Heating Ventilation and Air Conditioning (HVAC) system. This study provides energy loss signatures that can be used to diagnose a specific faulty component at the unit level. An analysis has been performed on a single air handling unit (AHU) to determine how faulty HVAC components effect the overall energy consumption of the unit. To begin the process, the Engineering Laboratory building on the campus of the University of Oklahoma was modeled for the simulation, using EnergyPlus 7.0, Google Sketchup 8, and OpenStudio 0.6.0. The building has an existing single duct system with a 3hp AHU with a forward curved fan that discharges 2500 cfm (1.175 m3/s) that covers approximately 2809 square feet (261 m2). Inputs into the simulation included building constructions, architecture, internal loads, and external loads from weather, sun, and shade objects. Simulations were run using the stated software, and a base case was established for energy consumption. Next, components and variables on the AHU, such as minimum outside air intake, economizer outside damper control, cooling coil valve, duct work pressurization, and various sensors were individually modified to reflect a malfunction or inefficiency. The energy loss caused from these changes in inputs was quantified and analyzed for the purpose of establishing a graphical range of energy loss signatures associated with each faulty component. Building Engineers and operators will be able to not only detect the exact malfunction faster, but also to ascertain the associated energy loss cost associated with the fault. The results of the study will be used to automate an online energy monitoring fault detection and diagnosis process.

scholarly journals Fault Detection and Diagnosis of Air Handling Unit: A Review

2019 ◽  
Vol 255 ◽  
pp. 06001 ◽  
Author(s):  
Cheng Yew Leong
Keyword(s):  
Fault Detection ◽  
Air Conditioning ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Commercial Building ◽  
Air Handling Unit ◽  
Operations And Maintenance ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems ◽  
And Control

Air-conditioning systems consumed the most energy usage nearly 45% of the total energy used in commercial-building. Where AHU is one of the most extensively operated equipment and this device is typical customize and complex which can results in hardwire failure and controller errors. The efficiency of the system is very much depending on the proper functioning of sensors. Faults arising from the sensors and control systems are a major contribution to the energy wastage. As such faults often go unnoticed for extended periods of time until the deterioration in performance becomes great enough to trigger comfort complaints or total equipment failure. Energy could be reduced if those faults can be detected and identified at early stage. This paper aims to review of various existing automated fault detection and diagnosis (AFDD) methods for an Air Handling Unit. The background of AHU system, general fault detection and diagnosis framework and typical faults in AHU is described. Comparison and evaluation of the various methodologies will be reviewed in this paper. This comparative study also reveals the strengths and weaknesses of the different approaches. The important role of fault diagnosis in the broader context of air- conditioning is also outlined. By identifying and diagnosing faults to be repaired, these techniques can benefits building owners by reducing energy consumption, improving indoor air quality and operations and maintenance.

scholarly journals Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Jingjing Liu ◽  
Min Zhang ◽  
Hai Wang ◽  
Wei Zhao ◽  
Yan Liu
Keyword(s):  
Fault Detection ◽  
Clustering Analysis ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
Control Loop ◽  
One Dimensional ◽  
Air Handling Unit ◽  
Sensor Fault Detection ◽  
Diagnosis Method ◽  
Detection And Diagnosis

This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.

An Approach to Bringing Automated Fault Detection and Diagnosis (AFDD) Tools for HVAC&R Into the Mainstream

2019 ◽  
Author(s):  
Annika Hacker ◽  
Ravi Gorthala ◽  
Amy Thompson
Keyword(s):  
Energy Consumption ◽  
Fault Detection ◽  
Evaluation Method ◽  
Field Testing ◽  
Hvac Systems ◽  
Commercial Buildings ◽  
Fault Detection And Diagnosis ◽  
Utility Companies ◽  
Detection And Diagnosis ◽  
Testing And Evaluation

Abstract Heating, ventilation and air-conditioning (HVAC) systems can consume over 5 quads of energy annually, representing 30% of energy consumption in the U.S. in commercial buildings. Additionally, commercial refrigeration (R) systems add about 2 quads to commercial buildings energy consumption. Most HVAC systems have one or more faults (low/high refrigerant charge, valve leakage, condenser/evaporator fouling, filter/dryer restriction, economizer faults, controls faults) that result in increased energy consumption. Automated fault detection and diagnosis (AFDD) tools have been developed to address this national issue and many tools are commercially available. AFDD tools have the potential to save considerable energy for existing commercial RTUs, chillers and refrigeration systems. These devices can be used for both retro commissioning, and, when faults are addressed, continuous commissioning as well. However, there appears to be multiple market barriers for this technology. A key market barrier for this technology is the lack of awareness of AFDD products among potential customers. Most HVAC contractors are not familiar with the latest AFDD technologies and HVAC technicians lack skills regarding these technologies. Quantifying potential benefits to building owners is difficult since there are several FDD tools with varying capabilities. For instance, there are several FDD products ranging from handling just economizer faults to those that also handle full-blown refrigerant-side and air-side faults. Methods/algorithms used in FDD vary significantly. Even though there are efforts to develop standards, currently there are no standards/methods to define functions, capabilities, accuracy, and reliability of FDD tools. Moreover, most of the commercial AFDD tools have not been verified in the field independently. This paper presents a comprehensive approach to bringing HVAC AFDD tools into the mainstream. The approach involves demonstrating ten commercially available tools at ten different sites, independent testing and evaluation of the FDD tools, communication with various stakeholders, identifying market barriers, and assisting utility companies in developing incentive programs. This paper presents selection of AFDD tools, site identification, and field testing and evaluation method.

Impact of roof shading on building energy performance in warm & humid climatic places of India

2020 ◽  
pp. 50-64
Author(s):  
Kuladeep Kumar Sadevi ◽  
Avlokita Agrawal
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Performance ◽  
General Assumption ◽  
Climatic Conditions ◽  
Building Envelope ◽  
Passive Cooling ◽  
Base Case ◽  
U Value ◽  
The Impact

With the rise in awareness of energy efficient buildings and adoption of mandatory energy conservation codes across the globe, significant change is being observed in the way the buildings are designed. With the launch of Energy Conservation Building Code (ECBC) in India, climate responsive designs and passive cooling techniques are being explored increasingly in building designs. Of all the building envelope components, roof surface has been identified as the most significant with respect to the heat gain due to the incident solar radiation on buildings, especially in tropical climatic conditions. Since ECBC specifies stringent U-Values for roof assembly, use of insulating materials is becoming popular. Along with insulation, the shading of the roof is also observed to be an important strategy for improving thermal performance of the building, especially in Warm and humid climatic conditions. This study intends to assess the impact of roof shading on building’s energy performance in comparison to that of exposed roof with insulation. A typical office building with specific geometry and schedules has been identified as base case model for this study. This building is simulated using energy modelling software ‘Design Builder’ with base case parameters as prescribed in ECBC. Further, the same building has been simulated parametrically adjusting the amount of roof insulation and roof shading simultaneously. The overall energy consumption and the envelope performance of the top floor are extracted for analysis. The results indicate that the roof shading is an effective passive cooling strategy for both naturally ventilated and air conditioned buildings in Warm and humid climates of India. It is also observed that a fully shaded roof outperforms the insulated roof as per ECBC prescription. Provision of shading over roof reduces the annual energy consumption of building in case of both insulated and uninsulated roofs. However, the impact is higher for uninsulated roofs (U-Value of 3.933 W/m2K), being 4.18% as compared to 0.59% for insulated roofs (U-Value of 0.33 W/m2K).While the general assumption is that roof insulation helps in reducing the energy consumption in tropical buildings, it is observed to be the other way when insulation is provided with roof shading. It is due to restricted heat loss during night.

Sign in / Sign up

Export Citation Format

Share Document