• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• Journal of Biosystems Engineering
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• v.34 no.6
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• pp.470-475
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• 2009

This study was performed to present the diagnosis basis of cooling performance deficiency according to air quantity included in refrigerant of air-conditioner by detecting the temperatures and pressures of refrigerant pipeline. The car air-conditioner of SONATA III (Hyundai motor Co., Korea) was tested by maximum cooling condition at 1500 rpm of engine speed in the room with controlled air condition at $33\sim35^{\circ}C$ and 55~57% RH. Measured variables were temperature differences between inlet and outlet pipe surface of the compressor (Tcom), condenser (Tcon), receive dryer (Trec) and evaporator (Teva), and high pressure (HP) and low pressure (LP) in the refrigerant pipeline, and temperature difference (Tcoo) between inlet and outlet air of the cooling vent of evaporator. Control variables were the refrigerant charging weight and the vacuum degree in the refrigerant pipeline before charging refrigerant. From the test, it was represented that the measuring values of (Tcom), LP and (Tcoo) were enabled to make the diagnosis of cooling performance deficiency according to quantity included in refrigerant of air-conditioner. The ranges of Tcom, LP and Tcoo to make the diagnosis of cooling performance deficiency were respectively less than $55^{\circ}C$, more than 166.7 kPa-g(1.7 kgf/$cm^2$) and less than $13.7^{\circ}C$. In the case of using only external sensors and the condition under the normal performances of air conditioner, it was considered that the ranges of LP and Tcoo to make the diagnosis of cooling performance deficiency were respectively more than 166.7 Pa and less than $12^{\circ}C$.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.11
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• pp.125-129
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• 2004

For the diagnosis of compressor abnormal condition in air-conditioner, AE signal which is derived from wear condition, compressed air and assembly error is analyzed experimentally. The burst and continuous type AE signal occurred by metal contact and compressed air and AE raw signal of compressors were directly acquired in production line. After extracting samples according to waveforms, Early Life Test(ELT) is conducted and classified to normal and abnormal waveform. The efficient parameters of waveform pattern are investigated in time and frequency domain and the diagnosis algorithm of air-conditioner by Neural Network estimation is suggested.

• Journal of Biosystems Engineering
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• v.36 no.5
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• pp.319-325
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• 2011

This study was conducted to figure out the diagnosis basis of cooling performance depending on water amount in the refrigerant of air conditioner, which can be estimated by the temperatures and pressures along the refrigerant circulation line. A car air conditioner of SONATA III (Hyundai motor Co., Korea) was tested at maximum cooling condition at the engine speed of 1500 rpm in the room controlled at 33~$35^{\circ}C$ air temperature and 55~57% relative humidity conditionally. Measured variables were temperature differences between inlet and outlet pipe surfaces of the compressor, condenser, receive drier and evaporator; and high pressure and low pressure in the refrigerant circulation line; and temperature difference between inlet and outlet air of the cooling vent of evaporator. In this study, changes of the water amount in the refrigerant were correlated to the temperatures and pressure changes and also water amount caused poor cooling performance. As water amount increased in the refrigerant in the air conditioner, the performance of the cooling or the heat transfer became worse. Temporal variations of the surface temperature of the evaporator outlet pipe and the low-side pressure showed various patterns that could estimate the water amount. When the water amount caused bad cooling performance, the patterns of the temperature of the evaporator outlet pipe indicated irregular fluctuation greater than $5^{\circ}C$. When the diagnosis system is using just external sensors of the low-side pressure and the temperatures of inlet and outlet air of cooling vent of the evaporator, the precise pattern of bad cooling performance caused by excess water amount in the cooling line was irregular pressure fluctuation, 25 kPa under 120 kPa, and temperature, $12^{\circ}C$ and less.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.7
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• pp.560-566
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• 2002

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this study, two different types of faults in the air-conditioning system, such as the condenser fouling and the evaporator fan slowdown, were considered. The neural network algorithm combined with data preprocessor was developed and applied to detect the faults of the real system. Test results show that this method is very effective to detect the faults in the air-conditioning system. Therefore, this developed method can be used for the development of the air-conditioner fault detection system.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1568-1575
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• 2013

There has been an increasing interest in the market of vehicle maintenance and repair equipments to decrease air pollution. However, most of the existing air conditioning system equipment in Korea have poor performance as well as non-protection against air pollution. The purpose of this paper is to develop the monitoring technology of recovering and recharging refrigerant for air conditioning system, and also to develop its related diagnostic system. This technology and system can supply the exact amount of refrigerant from the charger to the air conditioning system by precisely diagnosing and monitoring their statuses. This technology can also control recovering and recharging of refrigerant exactly by altering the recovering pressures of refrigerant according to circumstance temperatures.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2070-2075
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• 2008

Identification of steady-state is the first step in developing a fault detection and diagnosis (FDD) system. In a complete FDD system, the steady-state detector will be included as a module in a self-learning algorithm which enables the working system's reference model to "tune" itself to its particular installation. In this study, a steady-state detector of a residential air conditioner based on moving windows was designed. Seven representing measurements were selected as key features for steady-state detection. The optimized moving window size and the feature thresholds was suggested through startup transient test and no-fault steady-state test. Performance of the steady-state detector was verified during indoor load change test. From the research, the general methodology to design a moving window steady-state detector was provided for vapor compression applications.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.282-287
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• 2008

Identification of steady-state is the first step in developing a fault detection and diagnosis (FDD) system. In a complete FDD system, the steady-state detector will be included as a module in a self-learning algorithm which enables the working system's reference model to "tune" itself to its particular installation. In this study, a steady-state detector of a residential air conditioner based on moving windows was designed. Seven representing measurements were selected as key features for steady-state detection. The optimized moving window size and the feature thresholds was suggested through startup transient test and no-fault steady-state test. Performance of the steady-state detector was verified during indoor load change test. From the research, the general methodology to design a moving window steady-state detector was provided for vapor compression applications.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.4
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• pp.333-339
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• 2010

Identification of a steady state is the first step in developing a fault detection and diagnosis (FDD) system of a heat pump. In a complete FDD system, the steady-state detector will be included as a module in a self-learning algorithm, which enables the working system's reference model to "tune" itself to its particular installation. In this study, a steady-state detector of a residential air conditioner based on moving windows was designed. Seven representative measurements were selected as key features for steady-state detection. The optimized moving-window size and the feature thresholds were decided on the basis of a startup-transient test and no-fault steady-state test. Performance of the steady-state detector was verified during an indoor load-change test. In this study, a general methodology for designing a moving-window steady-state detector for applications involving vapor compression has been established.