• Tribology and Lubricants
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• 제36권6호
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.

• 암석학회지
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• 제23권2호
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• pp.119-138
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• 2014

활화산을 감시하는 방법은 육안으로 직접 관찰하는 방법, 과거의 분화 기록 문건을 참조하는 방법, 직접적으로 관측 장비를 동원하여 화산체를 감시하는 방법 등이 있다. 이 중 관측 장비를 이용하여 화산체를 감시하는 방법 중 가장 기본적인 것은 지진활동도의 감시이며, 이외에도 지진계에 기록된 지진활동 중 인위적인 노이즈를 걸러내는 데 효과적인 공진 관측, 그리고 정밀수준기, 전자거리측정기, 경사계, GPS, InSAR 관측법을 통한 지표변형의 감시, 화산가스 감시, 수문학적/기상학적 감시, 기타 지구물리학적 감시 등의 방법이 있다. 이러한 감시 기법을 통해 화산의 활동을 효과적으로 감시하고 이를 통해 지하 마그마방에서의 마그마 거동을 파악함으로써 미래의 화산 분화를 보다 정확하게 예측하고 조기 경보하여 그에 따른 재해의 피해를 경감하고 최소화 할 수 있다.

• 한국산학기술학회논문지
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• 제20권1호
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• pp.440-446
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• 2019

우리나라는 1988년 부산에서 처음 소나무 재선충병이 발생하였으며, 산림청은 국내 소나무림의 보호 및 산림자원의 확보를 위해 2005년에 소나무 재선충병에 대한 특별법을 제정하였다. 현재 소나무 재선충병의 발생 빈도는 증가추세를 보이고 있으며, 발생지역 확대를 막기 위해 화학적 통제 및 물리적 통제 기술이 적용되고 있다. 소나무 재선충병의 방제를 위해서는 먼저 피해 상황을 파악하고 주변 환경 및 특성을 고려한 최적의 방제 계획 수립이 필요하며, 넓은 지역에 대한 모니터링 방안으로 최근 UAV(Unmanned Aerial Vehicle)에 대한 관심이 증가하고 있다. 이에 본 연구에서는 UAV 기반 산림 모니터링 방법에 대한 경제성을 평가하고자 하였다. 기존 인력에 의한 모니터링 방법과 UAV를 적용한 방법의 효율성을 분석 한 결과, UAV 기반 산림 병해충 감시 방법은 기존 방법에 비해 약 50%의 비용 절감 효과가 있으며, 산림 조사의 누락 지역을 줄이는데도 도움이 될 것이며, 향후 추가적인 연구를 통해 UAV 기반의 산림 모니터링 방안의 검증이 이루어진다면 산림 조사 관련업무의 효율성이 크게 증가할 것이다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.452-461
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• 2008

Previous settlement prediction method based on settlement monitoring such as hyperbolic, monden method were developed under instantaneous loading condition and have restriction to be applied to soft ground under ramp loading condition. In this study, settlement prediction method under ramp loading was developed. New settlement prediction method under ramp loading considers influence factors of consolidation settlement and increase accuracy of settlement prediction using field monitoring data after ramp loading. Large consolidation tests for ideally controlled one dimensional consolidation under ramp loading condition were performed and the settlement behavior was predicted based on the monitoring data. As a result, new prediction method is expected to have great applicability and practicability for the prediction of settlement behavior.

• Nuclear Engineering and Technology
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• 제43권4호
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.16-21
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• 2020

How accurately reproducible energy is delivered to the wafer in the process of making thin films using PE-CVD (Plasma enhanced chemical vapor deposition) during the semiconductor process. This is the most important technique, and most of the reaction on the wafer surface is made by thermal energy. In this study, we studied the method of monitoring the change of thermal energy transferred to the wafer surface by monitoring the temperature change according to the change of the thin film formed on the showerhead facing the wafer. Through this research, we could confirm the monitoring of wafer thin-film which is changed due to abnormal operation and accumulation of equipment, and we can expect improvement of semiconductor quality and yield through process reproducibility and equipment status by real-time monitoring of problem of deposition process equipment performance.

• 한국환경과학회지
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• 제23권7호
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• pp.1307-1320
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• 2014

A number of problems associated with environmental noises in urban areas have significantly been considered. Specific measurement and estimation of the environmental noise became a primary issue in local governments. Environmental noise monitoring system is required in order to estimate and verify the a city noise map. However, current monitoring positions may not perfectly represent and incorporate many different view points, such as districts of a city, different utilizations of a city by the law, populations, and classifications and traffics of roads. In addition, scientific method to provide specific noise monitoring positions my not be avaliable in current literature. For this reason, the primary objective of this paper is to propose a new method for introducing a number of monitoring positions in the entire city. First, the quality function deployment (QFD) method was utilized to simultaneously represent both districts and utilizations of a city. Second, a new algorithm to find a number of monitoring positions was proposed by compromising many different view points: populations, classifications of roads and areas, and traffics of roads. Finally, the proposed monitoring positions and a sample noise map was provided for verification purposes. Based on these results, the proposed algorithm including the QFD concept may successfully provide specific noise monitoring positions by simultaneously consider may different view points and requirements of a city.

• Computers and Concrete
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• 제20권5호
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Structural Monitoring and Maintenance
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• 제1권1호
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• pp.69-87
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• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• Structural Monitoring and Maintenance
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• 제1권1호
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.