• Proceedings of the KIEE Conference
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• summer
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• pp.1278-1280
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• 2004

In this paper, we propose a new diagnosis method of DC/DC converter aging. The method is based on the variations of parasitic resistor for the aging process. We apply an on-line diagnosis of DC/DC converter because the observation is not a device, but a system. This study proposes a method of DC/DC converter diagnosis by analyzing the variations of model on the variations of parasitic resistor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.189-194
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• 2012

The main purpose of reactive power compensation monitoring system is to manage factory electrical installation efficiently by On-Off switching reactive power compensation equipment. The existing reactive power compensation monitoring system is only able to be managed by operator whenever electrical installation needed reactive power. Therefore, it may be possible for propagating the installation's faults when operator make the unexpected mistakes. To overcome the unexpected mistakes, in this paper, the author presents a reactive power compensation monitoring system for factory electrical installation using active database. by using active database production rule, stated system can minimize unexpected mistake and can operate centralized monitoring system efficiently. Test results on the five factory electrical installations show that performance is efficient and robust.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.3
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• pp.307-312
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• 2001

Ultrasonic signals from Charpy impact test specimen have been analyzed in order to evaluate the integrity of reactor pressure vessel. Base and weld metal that were extracted from reactor vessel doting plant outages according to the schedule of the surveillance test required by the related regulations have been used and the ultrasonic test parameters including velocity, attenuation, etc. showed a close correlations with the amount of neutron irradiation for base metal, relatively homogeneous materials. This result showed certain possibility where a nondestructive method could be used to predict the fluence of the Irradiation due to neutron in nuclear reactor vessel materials.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.4
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• pp.278-284
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• 1997

EMAT는 비접촉으로 초음파를 송수신 할 수 있는 탐촉자로서 시험체와 탐촉자간의 접촉을 위한 매개 물질이 필요치 않으므로, 움직이고 있는 물체에 초음파탐상법을 적용하고자 하는 분야와 초음파의 속도를 정밀하게 측정하고자 하는 분야에 주로 응용된다. 구체적으로는 길이가 긴 튜브류의 결함 탐상, 용접중인 재료의 용접상태 감시, 기차바퀴 및 레일의 결함 탐상, 고온상태인 재료의 결함 탐상 등이 비접촉 특성을 이용하여 적용될 수 있는 분야이며, 재료의 집합조직 및 소성이방성의 측정, 재료의 미세조직 및 기계적 강도의 예측, 그리고 잔류응력의 측정 등이 정밀한 초음파속도 및 감쇠의 측정으로부터 적용될 수 있는 분야이다. EMAT가 일반적인 접촉식초음파탐상법에 비하여 특별한 분야에의 응용에 큰 장점을 가지고 있지만, 낮은 에너지 전환효율, 넓은 불감영역, 그리고 사용주파수의 한계 등의 문제를 가지고 있기 때문에 기존의 접촉식 방법의 적용이 용이한 분야에의 적용은 필요하지 않다. 그러나 특별한 목적과 용도에의 적용 필요성이 생길 경우에는 적절한 연구를 통하여 알맞은 탐촉자를 제작하고 탐상 방법을 개발함으로서 본래의 목적에 알맞은 탐상이 수행될 수 있다.

• Journal of Welding and Joining
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• v.12 no.1
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• pp.59-72
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• 1994

This paper presents an on-line quality monitoring and control method to obtain a uniform weld quality in gas metal arc welding (GMAW) processes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to assess the integrity of the weld quality. Since a good quality weld is characterized by a relatively high depth-to-width ratio in its dimensions, the second geometrical parameter is regulated to a desired one. The monitoring variables are the surface temperatures measured at various points on the top surface of the weldment which are strongly related to the formation of the weld pool The relationship between the measured temperatures and the weld pool size is implemented on the multilayer perceptrons which are powerful for realization of complex mapping characteristics through training by samples. For on-line quality monitoring and control, it is prerequisite to estimate the weld pool sizes in the region of transient states. For this purpose, the time history of the surface temperatures is used as the input to the neural estimator. The control purpose is to obtain a uniform weld quality. In this research, the weld pool size is directly regulated to a desired one. The proposed controller is composed of a neural pool size estimator, a neural feedforward controller and a conventional feedback controller. The pool size estimator predicts the weld pool size under growing. The feedforward controller compensates for the nonlinear characteristics of the welding process. A series of simulation studies shows that the proposed control method improves the overall system response in the presence of changes in torch travel speed during GMA welding and guarantees the uniform weld quality.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.76-81
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• 2020

Recently, fire and explosion accidents caused by sparks scattered during welding and melting work in the work place where flammables are present. The causes of such fire accidents are mostly non-compliance with basic safety rules such as the removal of hazardous goods and the prevention of sparks scattering. It is strongly recommended to revise Industrial Safety and Health Act. This study analyzes the fire and explosion accidents in the work of firearms, such as welding and melting work, and analyzes the causes from a system perspective, and proposes an improvement plan for the system such as expanding the number of fire monitors, pre-approval of fire risk work, and intensifying fire prevention safety education.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.406-408
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• 1997

This paper presents the new method of a nondestructive spot welding state inspection system using neural networks. The learning process of neural networks makes the inspection system to adapt the variable welding parameters. The inspecting process is working with on-line real-time after off-line learning process. This neural network based inspection system shows reliable results through the field test for variations of applied voltages, currents, and contact area of the welding electrode.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.644-653
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• 1998

In resistance spot weld, the assurance of weld quality has been a long-standing problem. Since the weld nuggets if resustance spot welding form between the workpieces, visual detection of defects in usually impossible. Welding quality of resistance spot welding can be verified by non destructive and destructive inspections such as X-Ray inspection and testing of weld strength. But these tests, in addition to being time-consuming and costly, can entail risks due to sampling basis. The purpose of this study is the development of the monitoring system based on fuzzy inference, aimed at diagonosis of quality in resistance spot welding. The fuzzy inference system consists of fuzzy input variables, fuzzy membership functions and fuzzy rules. For inferring the welding quality(strength), the experimental data of the spot welding were acquired in various welding conditions with the monitoring system designed. Some fuzzy input variables-maximum, slop and difference values of electrode movement signals-were extracted from the experimental data. It was confirmed that the fuzzy inference values of strength have a .${\pm}$5% error in comparison with actual values for the selected welding conditions(9-10.5KA, 10-14 cycle, 250-300 $kg_f$). This monitoring system can be useful in improving the quality assurance and reliability of the resistance spot welding process.

• Journal of Welding and Joining
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• v.11 no.2
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• pp.27-41
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• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.515-522
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• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.