Automated Fault Detection, Diagnostics, Impact Evaluation, and Service Decision-Making for Direct Expansion Air Conditioners

This work describes approaches for automatically detecting, diagnosis, and evaluating the impacts of common faults in unitary rooftop air conditioning equipment. A semi-empirical component-based modeling approach using virtual sensors has been implemented using low-cost microcontrollers and tested on fixed-speed and variable-speed equipment using laboratory psychrometric test chambers. A previously developed virtual refrigerant charge sensor was applied to a fixed-speed rooftop unit with combinations of condenser types and expansion valve types and resulted in average prediction errors less than 10%. In addition, a methodology was developed that can be used to tune the empirical parameters of the model using data collected without psychrometric chambers, greatly reducing the experimental effort and costs required for the model. Virtual sensors previously developed for fixed-speed systems were also implemented for a variable-speed rooftop unit without significant loss of accuracy. Much of this work has been devoted to estimating the performance impacts of faults that grow over time, like heat exchanger fouling or refrigerant charge leakage. To estimate these impacts, semi-empirical models for predicting the normal performance of fixed-speed and variable-speed systems have been developed and evaluated using experimentally collected data. In addition, the virtual sensor approaches for estimating the actual performance of systems using low-cost sensor measurements were evaluated. Together, normal performance models and virtual sensor estimations were used to estimate the overall impacts of several faults on system performance. A methodology for quantifying the performance impacts of simultaneously occurring faults has been developed and tested using a detailed system model and experimental results. While relatively simple, simulated and experimentally collected results showed the fault impact models were accurate within 10% of the actual fault impacts. The fault impact evaluation models could be embedded in an AFDD system and used to determine when performance degradation faults should be serviced from an operating cost perspective. In addition, different service and maintenance strategies are compared in this work using a simulation environment that was developed. A data-driven artificial neural network model of a rooftop unit with faults has been derived for this purpose using a detailed fault impact model for direct expansion cooling equipment. This model was coupled with a building model to simulate operating cost impacts of performance degradations and service over the life of cooling equipment. An optimization problem was formulated with the goal to minimize lifetime energy and service costs and was solved using dynamic programming. Using the optimal solution as a baseline, suboptimal service decision-making strategies were implemented and simulated using the building model. It was found that condition-based maintenance strategies using the outputs of automated fault detection and diagnostics tools can significantly reduce lifetime operating costs over periodic service policies

PROBABILISTIC FAULT DETECTION AND DIAGNOSTICS FOR PACKAGED AIR-CONDITIONER OUTDOOR-AIR ECONOMIZERS

Poor economizer control, economizer damper failure, and excess outdoor-air contribute to these performance degradations. In order to promote optimal rooftop air-conditioner (RTU) performance and reduce operating costs, an automated fault detection and diagnostics (AFDD) tool has been designed for RTUs with integrated economizers. Based on previously proposed methods, the proposed method advances the economizer fault detection and diagnosis components by using statistical classifiers in order to provide more robust, probabilistic fault outputs. A set of air-side virtual sensors has also been added to the method in order to expand the applicable range of conditions fault detection and diagnostics can be applied. The operational performance of the outdoor-air damper was characterized using a series of laboratory tests in order to model the expected outdoor-air fraction at different damper actuator control signals and ambient conditions. Two temperature correction models were developed in order to minimize the sensor error caused by stratification. The first correction was to the outdoor-air temperature sensor. This sensor was influenced by return-air that was recirculated back into the outdoor-air stream, an effect of economizer hood design. The second temperature correction modeled was for the single-point mixed-air temperature. At the mixed-air temperature sensor location, significant thermal stratification and non-uniform flow is present due to ineffective mixing in the RTU mixing box. Finally, the temperature rise across the indoor fan was modeled, along with the expected mass-air flow rate and power consumption of the indoor fan. Using these models of normal performance, deviations from normal are detected using a fault detection classifier. Using a Bayesian classifier a comparison of expected and actual performance is made when the RTU operates at steady-state. Outdoor-air damper position faults and temperature sensor faults, including faults in the outdoor-air, return-air, mixed-air, or supply-air temperature measurements, are considered by the AFDD tool. After a fault has been detected, an active economizer diagnostic procedure is performed by sweeping the outdoor-air damper from the fully-closed to fully-open position. When the damper is at these positions, redundant system measurements can be compared and a set a fault diagnosis residuals can be calculated. These residuals yield unique responses to different faults when they are present in the system. Using this as a guide, faults are isolated using a statistical fault diagnosis classifier. Experimentally collected data were used to test the effectiveness of the AFDD method under different normal and faulty conditions. The false alarm rate of the fault detection method was approximately 1.0 %. The misdiagnoses rate of the diagnosis classifier for normal data was approximately 4.9 %. When taken together, the overall false alarm rate of the AFDD tool was approximately 0.05 %. This low false alarm rate can be attributed to the accuracy of the temperature sensor correction and outdoor-air fraction models that can be attained when using experimentally obtained training data for an individual RTU. This also shows of the advantage of embedding diagnostics into the equipment over a tool that is applied retroactively. The diagnosis tool was also able to correctly identify greater than 90 % of the different faults studied. The most significant faults studied, stuck outdoor-air damper faults, were correctly diagnosed in 93.2 % of the fault cases. As a first step towards determining optimal FDD thresholds, several performance tests were conducted in the laboratory in order to observe the affects of a stuck damper fault on system performance. Tests with warm, humid outdoor-air temperatures were considered. Different damper positions were tested and their impact on the outdoor-air fraction entering the system were examined. The damper faults were shown to increase system capacity and efficiency due to the higher evaporation temperature caused by the higher fraction of warm outdoor-air at the evaporator air inlet. However, a negative impact on required RTU run-time was also determined, yielding increases in required energy consumption in order to meet equivalent conditioned space loads. The cause of this increased run-time was the increased ventilation load component introduced by the opened damper. These conditions lead to a reduction in available cooling capacity to meet the space load. (Abstract shortened by UMI.

Development of Economic Impact Models for RTU Economizer Faults

Stuck outdoor-air dampers can lead to significant energy waste when undiagnosed for extended periods of time. This is especially true for packaged (rooftop) air conditioners where preventative maintenance may not be frequent or only reserved for emergencies. Automated fault detection and diagnosis (AFDD) tools for outdoor-air dampers and economizers have been proposed in the past to reduce the effort and cost for this kind of maintenance and are even required by some new building standards (CA - Title 24 2013). While qualitatively, the effects of stuck damper faults are understood, much less has been written about these faults’ impacts on cooling cycle performance and actual operating costs. An investigation of incorrect outdoor-air fraction on cooling capacity, efficiency, sensible heat ratio (SHR), and run-time is presented. An evaluation of the commanded damper position based on economizer controller logic is used to capture impacts of stuck damper faults at the full range of position and under different ambient conditions. Using experimental data, these models are validated for a 4-ton rooftop air-conditioner (RTU) with integrated economizer. The combined effect of these impacts are analyzed based on air-side virtual sensors outputs and modified version of an economic performance degradation index (EPDI) first proposed by Li and Braun (2006). This performance index estimates fault impacts on operation costs as well as the added equipment costs due to the need to operate the air-conditioner longer. These economic performance impact outputs can be used in an optimal maintenance scheduling tool in future work

Development of an Embedded RTU FDD using Open-Source Monitoring and Control Platform

Previous research on automated fault detection and diagnostics (FDD) for HVAC systems has shown promising benefits like earlier detection and more accurate isolation of different faults. While most researchers, equipment manufacturers, and policymakers agree that HVAC system FDD is important and has the potential to reduce significant energy waste due to faulty system operation, widespread adoption of these tools has been slow. An automated fault detection and diagnosis system has been developed for packaged (rooftop) air conditioners based on the VOLTTRONTM monitoring and controls framework developed by the Department of Energy. The system implements a virtual-sensor-based FDD methodology capable of isolating common rooftop unit faults such as improper refrigerant charge level, heat exchanger fouling, liquid-line restrictions, and compressor valve leakage. A fault impact evaluation component has also been implemented in order to determine the relative impact that faults have on system performance. This is accomplished using virtual sensor outputs and manufacturers’ performance map reference models for performance indices such as cooling capacity and COP. This system has been implemented using low-cost electronics components and was be tested using a 5-ton RTU in a laboratory environment. In this work, a high-level overview of the automated rooftop unit (RTU) FDD system structure will be presented detailing how individual software agents interact along with a description of the computational and network requirements of the system. Alternative system architectures will also be discussed in comparison to the hybrid system presented. A review of the FDD algorithms is also presented that details the virtual sensors implementations along with the methodology to detect, diagnose, and evaluate different faults.  Finally, the performance of the FDD system will be demonstrated using laboratory test data collected for a 4-ton RTU with micro-channel condenser. The goal of this research is to produce a field ready FDD tool for RTUs that can be used to show the benefits of FDD in real systems. Ultimately, the software implementation (using Python) and hardware designs of all the systems components will be released under an open source license in an effort to reduce the engineering effort required by equipment manufacturers interested in a complete AFDD solution

Artificial Neural Networks for Fast Rooftop Unit Fault Impact Modeling and Simulation

Like any electromechanical system, direct-expansion (DX) air conditioners and heat pumps often develop faults over time that contribute to reduced operating efficiency, more frequent comfort violations, or even premature failure. Automated fault detection and diagnosis (AFDD) methods have been developed for these systems and much experimental effort has been undertaken for their evaluation. In order to reduce development costs required for AFDD technologies, additional research related to modeling DX equipment subject to faults has been undertaken. Investigation of AFDD methods in a virtual environment typically requires relatively detailed equipment models based in some part on thermodynamic principles. Because of these embedded constraints, simulation of faulty equipment operating performance can be time consuming and computationally intensive. In this work, meta-models based on previously developed greybox fault impact models for DX equipment have been developed using artificial neural networks. After tuning these neural network meta-models for different equipment, AFDD performance and fault impacts were simulated using a simple building load model. Significant computational speedups were realized over the original greybox equipment models without loss of significant accuracy. Ultimately through careful meta-model training, it is believed that using neural networks to approximate detailed, computationally-intensive equipment or building models may be useful in applications that require frequent model evaluations

Comparing Maintenance Strategies for Rooftop Units having Multiple Faults through Simulation

Maintenance strategies currently used for commercial building rooftop units (RTU) can be classified into two categories: reactive strategies and proactive strategies. In reactive strategies, maintenance and service is performed only when needed, e.g. when a system is unable to maintain setpoint. In proactive strategies, maintenance is scheduled at routine intervals to avoid service interruptions regardless of whether the system actually needs it. While these strategies could not be more different, it is unclear which strategy is more optimal. Moreover, whether one strategy is more optimal than the other more than likely depends on the application – contributing to much uncertainty. A third category of maintenance has been enabled by automated fault detection and diagnostics (AFDD) technologies that aims to provide building operators and service providers more detailed information about the actual state of equipment in the field. This third strategy, called condition-based maintenance, aims to optimize service and maintenance decisions throughout the life of equipment based on updated measurements of performance and service costs. In this work, these three types of maintenance strategies are compared using a commercial building simulation model utilizing a fault impact equipment model. Along with comparing different strategies under the same fault scenario, ambient conditions, and loads, optimal maintenance schedules are generated using dynamic programming. Benefits of a condition-based maintenance approach utilizing a suite of AFDD methodologies are highlighted with respect to reducing operating costs

The Effectiveness of using Total System Power for Fault Detection in Rooftop Units

Previous research has been presented that suggests using instantaneous system power measurements can be used to perform fault detection for rooftop units. The methodology generates a normal system power model using measurements of system power and outdoor-air temperature, which can be then used to determine if system performance has deviated due to the presence of faults. This work presents data collected from several rooftop units subjected to different faults and ambient conditions using psychrometric chamber test facilities. The results of the testing show that total system power is not very sensitive to many common faults affecting direct-expansion air-conditioning equipment. In fact, only condenser fouling faults increase instantaneous power at significant levels. Other faults, such as improper refrigerant charge level, liquid-line restriction, or compressor valve leakage, may lead to total power reduction and also, in general, have relatively weak impacts on total system power.  Virtual sensor measurements of total cooling capacity or coefficient of performance (COP) are more sensitive fault detection indicators than system power

Thermoeconomic Diagnosis Of Air Conditioning Systems: Experimental Assessment Of Performance And New Developments For Improved Reliability

In the last two decades, great progress has been made in improving the efficiency of air-conditioning equipment. In addition to improved performance of new equipment, there has been an increasing interest in technologies that can maintain performance over time. This has led to research and development of Fault Detection and Diagnosis (FDD) techniques for air conditioning systems, that can support building owners in scheduling cost-effective maintenance and repairs. Among FDD techniques, thermoeconomic diagnosis is a novel method for the identification of faults occurring in air conditioning systems. A very limited number of papers have focused on this topic, and the methodology is still at a very early stage of development. Thermoeconomic diagnosis is an exergy-based method to quantify the additional energy consumption (or the EER penalty) associated with individual or combinations of faults. It has been initially tested for very simple vapor compression systems through simulation, but has never been evaluated using experimental data. This work aims to assess the performance of thermoeconomic diagnosis using experimental data obtained from a five-ton variable-speed packaged rooftop air conditioning unit (RTU). The RTU was tested in psychrometric chambers under a wide range of operating conditions and fault levels. Three faults that are commonly found in rooftop systems were investigated: (i) evaporator fouling, (ii) condenser fouling and (iii) refrigerant undercharge. The experimental results were used as inputs in an equipment model, to characterize the exergy behavior of each component in presence of faults and apply the approach of Symbolic Exergoeconomics. Experimental results show the technique had difficulty in detecting some faults and its performance is quite sensitive to operating conditions. Based on these results, improvements to the FDD methodology based on empirical models of plant components are proposed. These improvements act to isolate the effects of operating conditions from the thermoeconomic effects of different faults, improving overall performance

Development and Evaluation of an Automated Virtual Refrigerant Charge Sensor Training Kit

Virtual sensors have previously been developed and demonstrated that can provide a low cost and relatively accurate estimation of the amount of refrigerant charge contained in packaged (rooftop) air conditioners.  One particular virtual refrigerant charge sensor approach uses four surface-mounted temperature measurements to determine suction superheat, liquid-line subcooling and evaporator inlet quality that are inputs to an empirical model for charge. The empirical parameters of the model are determined using linear regression applied to laboratory data collected from the system. In previous studies, extensive psychrometric chamber testing was required at different refrigerant charge levels and ambient conditions to obtain sufficient data for the regression. This testing is expensive for equipment manufacturers and it can be difficult to find available test facilities. The current work describes the development of an automated open lab training kit for calibrating the virtual refrigerant charge level sensor in an open laboratory space. The developed automated training kit algorithm has the ability to modulate the condenser and evaporator fans to simulate the effects of different ambient conditions and automatically add different amounts of refrigerant. The charge level is automatically adjusted and monitored using solenoid valves and a digital weighing scale. This approach reduces the human involvement to a great extent and eliminates the need for psychrometric chambers. An optimal set of test conditions has been determined using optimal experimental design techniques and implemented as a Python application. An Arduino microcontroller is used to continuously send data from the sensors to a personal computer which is used to supervise the process, including determining when the system has reached steady-state. The training kit has been applied to several different rooftop units in an open lab space. A comparison of the virtual refrigerant charge sensor accuracy and time/cost for calibration determined using the automated system and using psychrometric chamber test facilities will be presented in the paper

Load-based Testing to Characterize the Performance of Variable-Speed Equipment

The characterization of heating and cooling performance of HVAC&R equipment is required of all manufacturers for determining seasonal energy efficiency ratings. The current rating standards, e.g. AHRI Standard 210/240, CSA C656, determine seasonal energy efficiency (e.g., SEER) using a bin method along with data from steady-state tests at different operating conditions. These standards were originally developed for equipment with single or two-stage thermostat control, but have been incrementally updated to consider equipment with variable-speed compressors and fans. However, the equipment testing is performed using control overrides and doesn’t consider the interaction of the integrated controls with the equipment. As a result, the standard ratings don’t capture the full range of part-load operation and don’t necessarily reward manufacturers who have superior controllers. To address this issue, we have been working with the Canadian Standards Association (CSA) to develop and evaluate a load-based testing methodology for evaluating the seasonal performance of variable-capacity equipment. This new testing methodology could be applied to both variable-speed and staged equipment, enabling a more direct and fair comparison of their performance. The testing methodology involves emulating the response of a building’s sensible and latent loads to equipment controls by dynamically adjusting temperature and humidity setpoints of the psychrometric chamber reconditioning system. Convergence criteria have been developed to automate the overall testing methodology so that the equipment performance can be fully evaluated using short-term tests (e.g., 1 day). Ultimately, automated load-based testing could lead to a practical approach for capturing equipment performance models that could be used in energy simulation programs for determining more accurate and application specific performance ratings. This paper presents the overall methodology of load-based testing in addition to a discussion of some of the experimental results