• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.8
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• pp.787-795
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• 2001

An experimentation on the on-line fault detection and diagnosis(FDD) system has been performed with HVAC system in he experimental building constructed inside the large scale environmental chamber. Personal computer with a home-made FDD program by pattern recognition method utilizing artificial neural network was connected on-line via Ether-net TCP/IP to the supervisory control server for HVAC system. The FDD program monitored the HVAC system by 1 minuted interval. The results showed that he FDD program detected the sudden or abrupt faults such s those in fans, sensors and heater, etc.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.73-82
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• 2004

The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the net positive suction head(NSPH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based fault detection and diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 & 4.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.11
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• pp.1111-1117
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• 2011

This paper proposed a fault detection and diagnosis (FDD) algorithm using nonlinear regression models, focusing especially on heat exchanger faults. This research concerned four working modes: those with no fault, evaporator fault, condenser fault, and evaporator and condenser faults. This research used no fault mode data to create an FDD algorithm. Using the no fault mode data, correlation functions for predicting the degree of superheat or subcool of heat exchangers (an evaporator and a condenser) were derived. Each correlation function has five inputs and one output. Based on these correlation functions, it is possible to predict the degree of superheat or subcool of each heat exchanger under various working conditions. The FDD algorithm was developed by comparing the predicted value and the simulation value. The FDD algorithm works well in all four working modes.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.10
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• pp.687-696
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• 2010

In this study, FDD algorithm was developed using the normalized distance method and general pattern classifier method that can be applied to constant air volume air handling unit(CAV AHU) system. The simulation model using TRNSYS and EES was developed in order to obtain characteristic data of CAV AHU system under the normal and the faulty operation. Sensitivity analysis of fault detection was carried out with respect to fault progress. When differential pressure of mixed air filter increased by more than about 105 pascal, FDD algorithm was able to detect the fault. The return air temperature is very important measurement parameter controlling cooling capacity. Therefore, it is important to detect measurement error of the return air temperature. Measurement error of the return air temperature sensor can be detected at below $1.2^{\circ}C$ by FDD algorithm. FDD algorithm developed in this study was found to indicate each failure modes accurately.

• 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.

• International Journal of Air-Conditioning and Refrigeration
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• v.16 no.4
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• pp.137-144
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• 2008

This study deals with on-line fault detection and diagnosis(FDD) for heat exchangers of a variable speed refrigeration system(VSRS) based on current information. The current residual which is the difference between real detected current from current sensors and estimated current from no fault model was utilized to diagnose faults of the heat exchangers. Comparing to the conventional FDD of constant refrigeration system based on temperature and pressure information, the suggested FDD method shows better robustness to the VSRS which has a feedback control loop. Moreover the suggested method can be expected more precise and faster diagnosis of faults about heat exchangers. Throughout some experiments, the validity of the method was verified.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.1
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• pp.47-55
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• 2005

An experimental study has been peformed to develop a scheme for fault detection and diagnosis(FDD) in a vapor compression refrigeration system. This study is to analyze fault effect on the system performance and to find efficient diagnosis rules for easy determination of abnormal system operation. The refrigeration system was operated with a variable speed compressor to modulate cooling capacity. The FDD system was designed to consider transient load conditions. Four major faults were considered, and each fault was detected over wide operating load range by separating the system response to the load change. Rule-based method was used to diagnose and classify the system faults. From the experimental results, COP degradation due to the faults in a variable speed system is severer than that in a constant speed system. The method developed in this study can be used in the fault detection of refrigeration systems with a variable speed compressor.

• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.7
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• pp.552-559
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• 2002

The fault detection and diagnosis (FDD) technology may be applied in order to decrease the energy consumption and the maintenance cost of the air conditioning system. In this study, rule bases and curve fitting models were used to detect faults in an air handling unit. Gradually progressed faults, such as the fan speed degradation, the coil water leakage, the humidifier nozzle clogging, the sensor degradation and the damper stoppage, were applied to the developed FBD system. Simulation results show good detections and diagnoses of these faults. Therefore, this method may be effectively used for the fault detection and diagnosis of the air handling unit.

• Journal of IKEEE
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• v.7 no.1 s.12
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• pp.107-118
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• 2003

In this paper, dynamic control model is formulated by considering the geometrical structure of the deaerator storage tank in nuclear power plant and input-output flow rate at steady state, and we describe fault detection and diagnosis (FDD) scheme based on the adaptive estimator. The performance and effectiveness of the proposed FDD scheme are evaluated by applying real operating data obtained from the YOUNGKWANG 3 & 4 FSAR.