• Tribology and Lubricants
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• v.36 no.6
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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.

• The Journal of the Petrological Society of Korea
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• v.23 no.2
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• pp.119-138
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• 2014

There are various ways to monitor active volcanoes, such as the method of observing the activity of a volcano with the naked eye, the method of referring to the past eruptive history based on the historic records and the method of monitoring volcanoes by using observation equipment. The most basic method from the observation equipment-using methods to monitor volcanoes is seismic monitoring. In addition to this, the ways to monitor volcanoes are as follows: resonance observation which may be effective to remove artificial noises from the seismic activities that are recorded in the seismograph, ground deformation by using precision leveling, electronic distance measurement, tiltmeter, GPS, and InSAR observation method, volcanic gas monitoring, hydrologic and meteorological monitoring, and other geophysical monitoring methods. These monitoring methods can make volcanic activities effectively monitored, determine the behavior of magmas in magma chambers and help predict the future volcanic eruptions more accurately and early warning, thus, minimize and mitigate the damage of volcanic hazards.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.440-446
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• 2019

Pine wilt disease occurred for the first time in Busan in 1988 and the damage has since been increasing. In 2005, a special law was enacted for pine wilt disease by Korea Forest Service. Incidences relating to the forest pest had been frequent and chemical control as well as physical control techniques had been applied to control it. Therefore, there is a need to reduce the damage caused by the pine wilt disease through intensive management such as continuous monitoring, control, and monitoring based on active control as well as management measures. In this study, the UAV-based monitoring method was proposed as an economical way of monitoring the forest pest. The efficiency of the existing method and UAV method had been analyzed, and as a result the study suggested that UAV can be used for forest pest monitoring and indeed improve efficiency. The UAV-based forest pest monitoring method has a cost reduction of about 50% compared with the conventional method and will also help to reduce the area where the survey was omitted.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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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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• v.43 no.4
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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.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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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.

• Journal of Environmental Science International
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• v.23 no.7
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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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• v.20 no.5
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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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• v.1 no.1
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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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• v.1 no.1
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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.