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Refrigeration systems can experience operating faults that negatively impact their performance, such as reducing their ability to maintain the temperature setpoint, increasing energy consumption, or reducing the life of the equipment. To better understand the impacts on the physical operating characteristics of a system, a numerical model can be used. However, fully physics-based models require significant computational effort and detailed system information that is typically unavailable; whereas data-driven models require a prohibitively large amount of test data. In this paper a gray-box model is described, using a semi-empirical approach from the literature. It combines into a complete cycle the models for each component: compressor, heat exchangers, pipelines, expansion valve, and liquid line receiver. A set of 35 test data from laboratory experiments on a commercial walk-in freezer was used for training and validating the model. The liquid line receiver was found to provide particular challenges for charge estimation, but the overall model performs quite well and is able to predict the effects of faults on most operating variables with reasonable accuracy.

This paper proposes and demonstrates the use of inverse methods to estimate grain properties during deep-bed drying. An inverse analysis was performed to estimate the bulk density, specific heat and initial moisture content of cereal grains, using only grain temperature measurements as inputs. Grain temperature data obtained from numerically solving the direct problem were used to generate the temperature measurements. An iterative procedure, based on minimizing a sum of squares function with the conjugate gradient method and the discrepancy principle, was used to solve the inverse problem. A statistical analysis was performed to evaluate the accuracy of the estimated results. The effects of measurement errors and the sensor location on the inverse solution were also investigated. The close agreement between the exact and the estimated results shows the capability of the proposed method in estimating unknown parameters.

This paper describes the development of a method that can be used to simulate in a laboratory the effects of air side fouling of condensers from split system air conditioners. The method includes a...

Automated fault detection and diagnosis (AFDD) can highlight system faults that otherwise go unnoticed. Supermarkets have strong potential to benefit from AFDD because substantial energy and environmental impacts can be avoided by diagnosing faults in the refrigeration, air-conditioning, and lighting systems. Many methods have been proposed in scientific literature and patents, but adoption is not widespread. This paper describes a project in which an extensive review was conducted of existing AFDD methods, and two disparate methods were selected for further study. The methods include a rule-based method and a data driven method. Each method was tested and analyzed using curated measurement data from existing supermarkets with and without faults present. The rule-based approach is most effective when the AFDD performance index is a controlled variable. The data driven method can detect changes in energy consumption, but is not as effective when input variable have significant fluctuation during unfaulted operation.

This paper describes research into the effects of air side fouling on the performance of air-cooled condensers used in unitary air-conditioning equipment. It takes a novel approach by using field-f...

Thermostatic expansion valves (TXVs) are widely used in air-conditioning and commercial refrigeration systems. Accurate predictions of the refrigerant mass flow rate across a refrigeration cycle are desired in many engineering processes and applications such as a computer simulation model, for control purposes, or for fault detection and diagnostics (FDD). Several researchers have developed mass flow models for TXVs. This paper reviews and analyzes four gray-box TXV mass flow models in the literature and evaluates and compares their performance. Three of the models were developed for air-conditioners, but in the current work their applicability to refrigeration system modeling is assessed, with the use of normal and faulted laboratory data from a commercial walk-in cooler and a freezer. Model regression parameters are calculated during the training process with the use of parameter estimation techniques and a training dataset. The models are then used to predict the mass flow rates for several validation data points. Each of the models is found to perform well for the majority of the data points for the freezer, typically with errors below 10%, but application of the models to the cooler's dataset resulted in relatively large errors. The source of the model deviations includes the difficulty associated with the calculation of the TXV diaphragm force and the thermodynamic states of a two-phase refrigerant at the inlet of the valve. The presence of a liquid line receiver, which are common in refrigeration equipment, appears to cause some model deviation.

Supermarkets undergo various types of operating faults that can require costly preventive maintenance, service, and repairs. Because supermarkets are growing in number and in the variety of system ...

Split system air conditioners are widely used to cool residential buildings, because of their low cost and simplicity. However, their efficiency is impacted by installation faults, which include: improper refrigerant charge (undercharge or overcharge), improper evaporator airflow, liquid line restrictions (LL), and the presence of non-condensable gas (NC) in the refrigerant. No known previously published research has studied the effect of these four faults on a system equipped with a microtube condenser, which has smaller tube size than a traditional condenser and therefore holds less refrigerant charge, but has a different configuration than a microchannel. Furthermore, very few have studied the impacts of LL and NC. This paper describes laboratory fault tests of a microtube-equipped system, compares the fault impacts with those of a traditional system, and considers the characteristic fault features. The tested system uses R-410A refrigerant and has a scroll compressor, a fixed orifice expansion device, and two fin-tube heat exchangers. The tests were carried out under steady operation with a range of fault intensities and operating conditions. The microtube system’s performance degradation from faults is similar to systems with traditional heat exchangers, despite the reduced capacity to hold refrigerant charge.

The effects of several types of faults on air conditioners and heat pumps have been studied in many laboratory experiments. All available data have been gathered, and the independent variable (fault intensity) and each of the dependent variables (fault impacts) are normalized to show the trends in a generalized fashion. Relationships are provided wherever there are sufficient results. Most of the significant fault types are included (except refrigerant charge variation, which was discussed in Mehrabi and Yuill (2017)): condenser heat transfer (CA), evaporator heat transfer (EA), liquid line restriction (LL), compressor leakage (VL), and non-condensables in the refrigerant (NC). Relationships are presented separately for fixed orifice and thermostatic expansion valve equipped systems. The variation level in the results indicates that in many cases, the generalized relationships provide reasonable predictors of fault effects on systems for which laboratory test results are unavailable. These relationships provide the first generalized fault effect models for air conditioners and heat pumps.

Faults in air-cooled vapor compression air-conditioning systems are known to reduce performance, including efficiency, capacity, and lifespan. Their effects have been studied, and fault detection and diagnostic (FDD) methods have been developed as tools for field technicians to install or repair systems, or for monitoring to alert operators to the fault’s presence. Most of this work has focused on faults that occur singly. It is likely that in some systems, multiple faults occur simultaneously, but it is uncertain what effects this may have on diagnostics. This paper describes a laboratory study of a split system air source heat pump in which combinations of two, three, and four simultaneous faults occur. The study includes all combinations of: improper evaporator airflow; overcharge or undercharge of refrigerant; liquid line restrictions; and non-condensable gas in the refrigerant, each at multiple fault intensities. Fault features – those characteristics that can be determined from measurements, for use in diagnostics – are analyzed, and the key fault features are presented. A robust existing method for determining refrigerant charge, the virtual refrigerant charge sensor (VRC) is tested using the multiple fault data, in order to understand how its performance is impacted by the combined faults. The VRC performs well, typically able to correctly determine whether a system is undercharged or overcharged, but the magnitude estimates are impacted. The results suggest that simple subcooling-based methods of charging a system are likely to provide unsatisfactory results when other faults are present.

Many automated fault detection and diagnostics methods have been developed for application to building mechanical systems over the past 20 years because they have the potential to reduce operating costs and energy consumption by providing early warning of performance degradation faults. Supermarkets could be a very beneficial setting to deploy automated fault detection and diagnostics, particularly in the refrigeration systems, which are major energy users and are known to commonly suffer from significant refrigerant leakage problems. The current article provides an overview of the common mechanical systems deployed in supermarkets, and then describes a comprehensive review of the literature on automated fault detection and diagnostics methods from other systems that could potentially be applied in supermarket settings. A collection of supermarket field data is analyzed in the context of its potential use in automated fault detection and diagnostics methods from other systems. The review includes methods ...

Performance degradation of air conditioners and heat pumps due to faults has been studied in several experiments reported in the literature. Among the faults, refrigerant charge variation is believed to be very common, and has been the focus of experiments by many researchers. In the current paper, using all available results in the literature, the effect of refrigerant charge faults on single-speed air-cooled air-conditioners and heat pumps is summarized. Generalized relationships have been developed to describe the fault effects, and these are provided for equipment operating at ANSI/AHRI 210/240 standard test conditions. Results are presented separately for fixed orifice (FXO) and thermostatic expansion valves (TXV), for both cooling and heating modes. The variation level of the results indicates that for many applications, it is reasonable to use these generalized relationships to estimate the effect of refrigerant charge variation on systems that have not been tested in a laboratory.

Air conditioning systems are known to experience operating faults, often caused by problems in their installation. To understand the impacts of these faults on system-wide performance requires laboratory tests with faults imposed, and many such experimental results have been published. However, it is likely that multiple simultaneous faults occur, but published results from testing with two simultaneous faults are very limited. This paper presents results from a laboratory study of an air-to-air heat pump system with combinations of: refrigerant charge (CH) too high or too low, evaporator airflow (EA), non-condensable gas (NC) in the refrigerant, and liquid line restrictions (LL). It provides the first published results of combinations of three and four simultaneous faults, in addition to previously untested types of double fault combinations. The impacts on cooling capacity and coefficient of performance (COP) are analyzed. COP reductions up to 34% occur within operation safety limits, meaning that such fault combinations would go unnoticed. An additional analysis examines the extent to which the superposition principle applies to combined fault impacts. The superposition principle is shown to have up to 25% error, indicating that there are synergistic effects – and sometimes cancelling effects – in the impacts from fault combinations.

A figure of merit, V, to characterize the value of an AFDD maintenance tool is presented. The AFDD tool is fed data representing air-conditioners operating with and without faults across a range of conditions. The calculation of V considers the probability of each scenario occurring, the tool's response, and the resulting implications. These results are summed to give a typical average value for deploying the AFDD tool, as compared to maintenance being performed without AFDD. Case studies illustrate the calculation of V. Six AFDD tools and two idealized tools are evaluated. Five of the six real tools give negative V, meaning that their use causes more harm than good. The sixth shows approximately $10 benefit per nominal ton of air-conditioner capacity per typical maintenance visit. This represents about half of the maximum potential value. These results demonstrate the importance of measuring AFDD performance and the potential monetary benefits of AFDD.

Automated fault detection and diagnosis (AFDD) tools are used to identify degradation faults that reduce the performance and life of air-conditioning equipment. A recent methodology has been developed to evaluate the performance of AFDD tools. The methodology involves feeding a library of input data to an AFDD protocol and categorizing the results. The current paper describes a study that has been conducted to assess the effect of using various input data sets in the evaluations. These input data sets include different distributions of fault type, fault intensity, and operating temperatures. Case study evaluations of three AFDD protocols in current widespread use are used to demonstrate the effects. The paper shows that evaluation results are sensitive to input data sets, and argues that data sets used in previously published studies should be improved to give higher fidelity evaluations. It concludes that for AFDD performance evaluation to be meaningful, the fault and operating conditions need to be controlled so that they connect to the anticipated deployment conditions. A related conclusion is that it is necessary to use simulation data, rather than laboratory measurement data, to conduct performance evaluation of AFDD.

Faults are believed to be common in split system air-conditioner and heat pump systems, due to operating degradation or installation problems. Common faults include improper refrigerant charge, reduced evaporator airflow, liquid line restrictions, and the presence of non-condensable gas. Laboratory tests were used to quantify fault impacts on performance of a heat pump system, and impacts on indicator variables such as refrigerant temperatures and pressures. This paper describes a large set of laboratory tests implemented on a high efficiency heat pump operating in cooling mode. This system uses R-410A refrigerant and has a rotary compressor, TXV, two accumulators, and a compensator. A small number of previous experimental studies have been done previously to study the effects of some operating faults, but none has examined a modern system with these components. The tests were conducted with a range of fault intensities and driving conditions. The results are compared to previous researchers’ experimental results. The system’s performance was found to be quite robust in the presence of faults. The TXV, accumulators, and compensator significantly reduce sensitivity to refrigerant charge and liquid line restriction faults, and other faults to a lesser extent.

This paper proposes and demonstrates a data-driven fault detection and diagnosis strategy for packaged rooftop units using statistical machine learning classification methods. The fault detection and diagnosis task is formulated as a multi-class classification problem. Seven typical rooftop unit faults are discriminated against one another as well as the normal condition. Since experimental data for rooftop units is rare and difficult to obtain, a simulated data library of model faults at steady state operation is used for training and validating the classifications models. Synthetic minority over-sampling technique is used to generate new artificial samples of minority class in order to balance the dataset. Nine well-known classification methods are applied to our dataset, and their performance is compared. The results show that support vector machine, with an overall accuracy rate of 96.2%, is the best classifier, and linear discriminant analysis, with an overall accuracy rate of 76.2%, is the worst classifier. The performance of the classification methods for individual faults is also characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables is also discussed. The high accuracy of the classification methods shows the potential of a data-driven strategy in detecting and diagnosing the rooftop unit faults.

Air source heat pumps have the potential to efficiently heat and cool buildings, but their effectiveness in many climates is limited by frost growth on the outdoor heat exchanger in heating mode. These heat exchanger coils are also subject to fouling from fibers and particulates outdoors. Frost and fouling have each independently been studied extensively, and newly available research on fouling provides a method to realistically foul the air-side of coils in the laboratory. The current paper uses this method to conduct the first laboratory study of the important interactions between fouling and frost growth for a heat pump outdoor coil. A microchannel heat exchanger is tested clean and fouled, with variations in fouling level, humidity, and initial face velocity of air. Air flow rate is allowed to vary with resistance, and a surprising result is that light fouling increases heat transfer and frost growth rates, even with a 3.5% reduction in initial airflow caused by the fouling. However, heavier fouling decreases both rates. When frost growth is normalized to heat transfer, it is found to be increased by fouling; i.e. the coil's sensible heat ratio decreases.

Automated fault detection and diagnosis (AFDD) has the potential to provide early warning of performance degradation faults before they might otherwise be apparent, and before they cause failure of the system. AFDD approaches have been developed in many industries, such as aerospace, process control, and air-conditioning. In air-conditioning applications the cost-sensitivity of the market requires that there is minimal cost premium for AFDD, so methods typically must be deployed with very few sensors to provide input and minimal engineering cost. In addition, because life safety is not a concern, less accurate methods can be tolerated. In this landscape, there are many methods that don’t perform well, but until recently there has been no standardized method or metrics to test or describe performance of AFDD. This chapter describes a new methodology, and a specific method to test and characterize the performance of AFDD tools that are applied to air-conditioning systems, and illustrates the methods with a case study. The widely used AFDD approach tested in the case study shows poor performance, which underscores the importance of evaluating AFDD performance.