• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• Journal of the Korean Magnetics Society
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• v.15 no.5
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• pp.292-297
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• 2005

Steam generator of a nuclear power plant has important rolls for the heat transfer and the isolation of radioactive materials. So bursting of the steam generator tube is directly related to the accident of nuclear power plants. Incone1600 has been used for the steam generator tube material. The material shows non-magnetic and metallic properties, eddy current NDT method has been employed for defects detection. In this work, a differential type of eddy current probe was developed to improve resolution of defect detection. To verify properties of the developed differential type eddy current probe, we have made reference material with SUS304 which has similar magnetic and electrical properties of Inconel600. Using the developed differential type eddy current probe, we can detect defect size of 0.25 mm in diameter and 0.2 mm in depth (volume of $1{\times}10^{-3}\;mm^3$) with the reference material.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1083-1090
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• 2011

A steam generator poses many difficulties during the development of a sodium-cooled fast reactor because of the sodium-water-reaction problems. Until now, several types of steam generators have been developed, but the specifications of these generators differed in each country. Moreover, even if a country had developed a steam generator, it was not used in the subsequent reactor because the current techniques were not stabilized to select the proper steam generator. As a common development, the Benson steam cycle with few welding locations and high economical efficiency may be adopted. Moreover, the design is dwelled on the convenience of inspection, detection, control, and maintenance for the wear caused by sodiumwater reactions. The specifications of the designed steam generators were reviewed and the current technical problems for steam generators were analyzed. Concepts were proposed to overcome the current technical problems for steam generators.

• Journal of the Korean Society for Nondestructive Testing
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• v.7 no.2
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• pp.16-26
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• 1988

In the in-service inspections for the steam generator tubing of the nuclear power plants by the Eddy Current Testing, the ECT signals are evaluated by their phase. If oxidized copper sludge is piled up in the secondary side, however, big sludge signals occur in large quantities which originate from copper layers forming in the sludge piles due to the pitting mechanism of the steam generator tubing by $Cu^{2+}$, and modulate the defect signals, causing the difficulty in the defect detection. In this research, sludge specimens were prepared considering the formations of the sludge signal sources and multi-frequency ECT mixing experiments by different choices of the mixing standards were performed. The results were found to be 5 to 30% of the tube wall thickness over-estimated. Experiments using the ring-type mixing standards showed the least errors of all, while those with the mixing standards nearing the sludge conditions brought larger errors as a result of the influence of the interference between the defect and the copper layers.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.4 no.2
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• pp.34-39
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• 2008

Steam Generator(SG) tubes are classified as one of the key components in nuclear power plants, and they should be periodically examined by the intensified management program for the assurance and diagnosis of their structural integrity. In this study, we use the optimum analysis technique to draw the detection and categorization of bowing(BOW) signals; abnormal tube-to-tube proximity in the SG upper bundle free span area. The locations in which BOW signals are detected likely have latent degradation of ODSCC(Outer Diameter Stress Corrosion Cracking). For the sake of timely and correct detection of BOW signals and diagnosis of ODSCC, we carried out the experimental demonstrations using a reduced mock-up. And we validated the MRPC(Motorized Rotating Pancake Coil) analysis technique is better than the bobbin. Hence, it comes to conclusion that the optimum analysis technique can be a good alternative for the reliable SG tube examination.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.107-112
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• 2005

This paper deals with a fault detection system design for uncertain nonlinear systems modelled as T-S fuzzy systems with the integral quadratic constraints. In order to generate a residual signal, we used a left coprime factorization of the T-S fuzzy system. From the filtered signal of the residual generator, the fault occurence can be detected effectively. A simulation study with nuclear steam generator level control system shows that the suggested method can be applied to detect the fault in actual applications.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.75-76
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• 2005

• Proceedings of the Korean Magnestics Society Conference
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• 2008.12a
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• pp.208.1-208.1
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• 2008

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1677-1688
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• 2020

In the sodium-cooled fast reactor, the steam generator is a heat exchange device between sodium and water, which may cause leakage, resulting in a sodium-water reaction accident, which in turn affects the safe operation of the entire nuclear reactor. To this end, the electromagnetic vortex flowmeter is used to detect leakage of the steam generator and its signal processing method is studied in this paper. The hydraulic experiment was carried out by using water instead of liquid sodium, and the sensor output signal of the electromagnetic vortex flowmeter under different gas injection volumes was collected. The bubble noise signal is reflected by the base line of the sensor output signal. According to the relationship between the proportion of the bubble noise signal in the sensor output signal and the gas injection volume, a signal processing method based on the energy ratio calculation is proposed to detect whether the water contains bubbles. The gas injection experiment of liquid sodium was conducted to verify the effectiveness of the signal processing method in the detection of bubbles in sodium, and the minimum detectable leak rate of water in the steam generator was detected to be 0.2 g/s.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.848-859
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• 2016

System failures in thermal power plants (TPPs) can lead to serious losses because the equipment is operated under very high pressure and temperature. Therefore, it is indispensable for alarm systems to inform field workers in advance of any abnormal operating conditions in the equipment. In this paper, we propose a clustering-based fault detection method for steam boiler tubes in TPPs. For data clustering, k-means algorithm is employed and the number of clusters are systematically determined by slope statistic. In the clustering-based method, it is assumed that normal data samples are close to the centers of clusters and those of abnormal are far from the centers. After partitioning training samples collected from normal target systems, fault scores (FSs) are assigned to unseen samples according to the distances between the samples and their closest cluster centroids. Alarm signals are generated if the FSs exceed predefined threshold values. The validity of exponentially weighted moving average to reduce false alarms is also investigated. To verify the performance, the proposed method is applied to failure cases due to boiler tube leakage. The experiment results show that the proposed method can detect the abnormal conditions of the target system successfully.