• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.1502-1508
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• 2011

Ground anchor expands the hollow wall of settled part and has the structure which resists the designed tensile load by the bearing pressure generated by the wedge of the anchor body pressing in the expanded part. Such ground anchor has been recognized for stability and economicality since 1960s in technologically advanced nations such as Japan and Europe, and in 1970s, the Japan Society of Soil Engineering has established and announced the anchor concept map. The ground anchor introduced in Korea, however, has the structural problem where the tensile strength is comes only from the ground frictional force due to expansion of the wedge body. In an interval where the ground strength is locally reduced due to fault, discontinuation or such, this is pointed out as a critical weakness where the anchor body of around 1.0m must resist the tensile load. Also, in the installation of concrete block, the concentrated stress of concrete block constructed on the uneven rock surface causes damage, and many such issues in the anchor head have been reported. Thus, in this study, by using the expanded bit for precise expansion of settled part, the ground anchor system was completed so that the bearing pressure of ground anchor can be expressed as much as possible, and the bearing plate was inserted into the ground to resolve the existing issues of concrete block. Through numerical analysis and pullout test executed for verification of site applicability, the pullout-behavior characteristics of anchor was analyzed.

• 비파괴검사학회지
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• 제35권3호
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• pp.185-192
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• 2015

동적하중에서의 베어링 결함에 대한 실시간 진단기술은 상대적으로 저조하다. 따라서 볼베어링의 이상상태 현상으로 인한 온도 상승 및 진동 증가 등을 사전에 검출하는 기술이 필요하며, 회전체에 대한 운전상태 감시 및 손상 진단을 통해 발전설비의 원활한 운전을 기할 수 있는 검출 기술이 필요하다. 적외선 열화상 실험과 더불어 ANSYS를 이용한 유한요소해석으로부터 실험과 동일한 베어링을 구조 설계 및 해석하여 데이터를 분석함으로써 열화상 기술로 얻은 데이터의 신뢰성을 확보하였다.

• 대한임베디드공학회논문지
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• 제19권3호
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• 한국지진공학회논문집
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• 제8권5호통권39호
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• pp.65-77
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• 2004

본 연구에서는 원전기기의 내진안전성을 향상시키기 위한 진동대 실험을 수행하였다. 원전 격납건물과 유사한 진동수 특성을 가지는 구조물을 제작하여 실험에 사용하였으며 구조물 내부에 설치된 기기를 모사 하기 위하여 면진장치가 설치된 강체블럭을 층바닥에 설치하였다. 주파수 특성이 상이한 3종류의 지진파를 이용하여 진동대 실험을 수행하였다. 면진장치로는 천연고무베어링(NRB)과 고감쇠고무베어링(HDRB)을 사용하여 고무의 감쇠특성에 따른 면진기기의 효율성을 분석하였다. 또한 입력지진동의 주파수 특성에 따라 적절한 면진장치의 기기면진효과를 평가하였다. 실험결과 적절한 면진장치를 사용함으로써 기기의 지진응답을 크게 줄일 수 있으며 지진에 대한 안전성을 향상키킬 수 있는 것으로 나타났다.

• 한국지능시스템학회논문지
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• 제18권5호
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• pp.718-723
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• 2008

베어링은 각종 설비에서 활용되는 중요한 기계요소 중 하나이다. 설비고장의 상당수는 베어링의 결함이나 파손에 기인하고 있다. 따라서 베어링에 대한 온라인모니터링기술은 설비의 정지를 예방하고 손실을 줄이는 데 필수적이다. 본 논문은 진동 신호를 이용하여 베어링의 상태를 예측하기 위한 온라인모니터링에 대해 연구한다. 프로파일로 주어지는 진동신호는 이산 웨이블릿 변환을 통해 분석되고, 분해수준별 웨이블릿 계수로부터 얻은 통계적 특징 중 유의한 것을 선별하고자 분산분석 (ANOVA)을 이용한다. 선별된 특징벡터는 Support Vector Machine (SVM)의 입력이 되는 데, 본 논문에서는 다중클래스 분류문제를 다루기 위한 계층적 SVM 트리를 제안한다. 수치실험 결과, 제안된 방법은 베어링의 결함을 분류하는 데 우수한 성능을 갖는 것으로 나타났다.

• 문화기술의 융합
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• 제9권3호
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• pp.845-850
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• 2023

본 논문에서는 전이학습을 이용하여 볼베어링의 진동진단을 수행하는 방법을 제안한다. 고장을 진단하기 위해 진동신호를 시간-주파수로 분석할 수 있는 STFT을 CNN의 입력으로 이용하였다. CNN 기반의 딥러닝 인공신경망을 빠르게 학습하고 진단 성능을 높이기 위해 전이학습 기반의 딥러닝 학습 기법을 제안하였다. 전이학습은 VGG 기반의 영상 분류 모델을 이용하여 특징 추출기와 분류기를 선택적으로 학습하였고, 학습에 사용한 데이터 세트는 Case Western Reserve University 대학에서 제공하는 공개된 볼베어링 진동 데이터를 사용하였으며, 성능평가는 기존의 CNN 모델과 비교하는 방법으로 수행하였다. 실험 결과 전이학습이 볼베어링 진동 데이터에서 상태 진단에 유용하다는 것을 증명할 수 있을 뿐만 아니라 이를 통해 다른 산업에서도 전이학습을 사용하여 상태 진단을 개선할 수 있다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 춘계학술대회 논문요약집
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• pp.315-315
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• 2004

자기베어링 시스템은 액츄에이터, 센서, 제어기, 전류앰프 등으로 구성되어 있으며 시스템의 신뢰도는 각 구성 요소의 신뢰도와 구성요소의 상호 작용에 의해 결정된다. 자기 베어링 기술이 현재 보다 많은 분야에 적용되기 위해서는 신뢰도의 향상이 필수적이다. 본 논문에서는 자기베어링에서 사용되는 센서 중 일부가 작동하지 않더라도 시스템이 정상적으로 작동하는 센서 고장 허용 자기 베어링 시스템을 기술하였다.(중략)

• Smart Structures and Systems
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• 제16권4호
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• pp.701-723
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• 2015

Friction isolators are one of the most important types of bearings used to mitigate damages of earthquakes. The adaptive behavior of these isolators allows them to achieve multiple levels of performances and predictable seismic behavior during different earthquake hazard levels. There are three main types of friction isolators. The first generation with one sliding surface is known as Friction Pendulum System (FPS) isolators. The double concave friction pendulum (DCFP) with two sliding surfaces is an advanced form of FPS, and the third one, with fully adaptive behavior, is named as triple concave friction pendulum (TCFP). The current study has been conducted to investigate and compare seismic responses of these three types of isolators. The structure is idealized as a two-dimensional single degree of freedom (SDOF) resting on isolators. The coupled differential equations of motion are derived and solved using state space formulation. Seismic responses of isolated structures using each one of these isolators are investigated under seven near fault earthquake motions. The peak values of bearing displacement and base shear are studied employing the variation of essential parameters such as superstructure period, effective isolation period and effective damping of isolator. The results demonstrate a more efficient seismic behavior of TCFP isolator comparing to the other types of isolators. This efficiency depends on the selected effective isolation period as well as effective isolation damping. The investigation shows that increasing the effective isolation period or decreasing the effective isolation damping improves the seismic behavior of TCFP compared to the other isolators. The maximum difference in seismic responses, the base shear and the bearing displacement, for the TCFP isolator are calculated 26.8 and 13.4 percent less than the DCFP and FPS in effective isolation damping equal to10%, respectively.

• 한국생산제조학회지
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• 제22권3호
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• pp.343-348
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• 2013

In line with the advances in factory automation, various pieces of equipment are now operated in batch processes controlled by computers. However, many kinds of faults can occur in complicated and large systems, which can result in low productivity and economic loss. The reliability and safety of systems have been studied because of the difficulty of determining the severity and location of faults. Therefore, it is necessary to detect and diagnose such faults in order to guarantee the reliability and safety of the equipment. In this paper, a diagnosis method for the ball bearings of a hydraulic pump is applied using a vibration signal for the maintenance of injection molding equipment. The bearings' defects are selected as a main failure mode through a failure mode and effect analysis (FMEA). Usually, there are nonlinear and impulse components of vibration in a ball bearing with faults. For the effective fault diagnosis of a ball bearing, nonlinear diagnostic methods and time-frequency analysis are applied, in addition to the methods currently used, such as power spectrum, time series analysis, and statistical methods. As a result of this study, a failure diagnosis system is provided that is useful even for non-experts. This is a condition-based method that makes it possible to resolve problems in a timely and economical way, in contrast to the prior method, which required regular but wasteful maintenance based on the experience of expensive external experts.

• 한국광물학회지
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• 제11권2호
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• pp.85-96
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• 1998

The sericite ore is formed by the hydrothermal alteration of rhyodacitic welded tuff. The alteration zone of the host rock can be classified into four types based on the mineral assemblages ; sericite, quartz-sericite, silicified and propylite zone. The sericite ore mainly occurs as vein types and fault clay along the fault plane in the quartz-sericite zone. Mineral components of the sericite ore are mainly sericite with minor diaspore, corundum and pyrite. The sericitic porcelaineous ore is mainly composed of quartz and sericite. Accessory minerals are muscovite, diaspore, sphene, corundum, pyrite, iron-oxides and etc. The chemical compositions of K2O, Al2O3, and ignition loss in the sericite ore increase largely than that of the host rock, while the compositions of SiO2, Na2O and Fe2O3 decrease. XRD patterns of the heat-treated sericite ores show the formation of mullite at $1,200^{\circ}C$. and the diaspore-bearing sericite ore forms mullite and corundum at $1,200^{\circ}C$. The differential thermal analysis of the sericite ores show small endothermic peak at 645~668$^{\circ}C$. and the diaspore-bearing sericite ore shows a strong endothermic peak at $517^{\circ}C$. It indicates that the decomposition of diaspore appear at lower temperature than that of sericite. The thermal expansivity of the sericite ores show the similar pattern. The sericite ores show the thermal expansivity of 3.3~4.7% at 900$^{\circ}C$ and 0.39~0.75% at 1,20$0^{\circ}C$, respectively. DTA-TG curves of the sericite ores show closely relations with the thermal expansivity.