• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2007년도 특별 심포지엄 논문집
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• pp.31-42
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• 2007

Dc resistivity monitoring has been increasingly used in order to understand the changes of subsurface conditions in terms of conductivity. The commonly adopted interpretation approach which separately inverts time-lapse data may generate inversion artifacts due to measurement error. Eventually the contaminated error amplifies the artifacts when reconstructing the difference images to quantitatively estimate the change of ground condition. In order to alleviate the problems, we defined the subsurface structure as four dimensional (4-D) space-time model and developed 4-D inversion algorithm which can calculate the reasonable subsurface structure continuously changing in time even when the material properties change during data measurements. In this paper, we discussed two case histories of resistivity monitoring to study the ground condition change when the properties of the subsurface material were artificially altered by injecting conductive materials into the ground: (1) dye tracer experiment to study the applicability of electrical resistivity tomography to monitoring of water movement in soil profile and (2) the evaluation of cement grouting performed to reinforce the ground. Through these two case histories, we demonstrated that the 4-D resistivity imaging technique is very powerful to precisely delineate the change of ground condition. Particularly owing to the 4-D inversion algorithm, we were able to reconstruct the history of the change of subsurface material property.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.92-95
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• 2019

Automation control and the data for control of industrial equipment for the diagnosis and prediction is a key to success in the 4th industrial revolution. It increases process efficiency and productivity through data collection, realtime monitoring, and the data analysis. However, university and research environment are still suffering from logging the data in manual way, and we occasionally loss the equipment data logging due to the lack of automatic data logging system. State variable presents the current condition of the equipment operation which is closely related to process result, and it is valuable to monitor and analyze the data for the equipment health monitoring. In this paper, we demonstrate the collection of equipment state variable data via programmable logic controller (PLC) and the visualization of the collected data over the Web access supervisory control and data acquisition (SCADA). Test vehicle for the implementation of the suggested SCADA system is a relay switched physical vapor deposition system in the university environment.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.343-346
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• 2008

To improve the reliability and extend the life for a wind-turbine gearbox, the gearbox needs to be monitored and analysed exactly. This study was conducted to analyze and detect the gearbox conditions when lubricating oil contaminated by wear particles was used. Characteristics of the gearbox failure by wear particles were monitored simultaneously by the on-line measurement sensor of vibration, oil condition and temperature. For the detail vibration analyses, frequency analysis(FFT) was performed. The results of the study were summarized as follows: Vibrational signal was found sensitive to abnormal changes of the gearbox conditions when lubricant was contaminated by wear particles. Also, using frequency analysis for the harmonics of gear mesh frequency(GMF), it is found that the failure of gearbox was caused by the damages of meshing gears. However, temperature and oil condition measuring signals were found not so effective to detect any gearbox failure by oil contamination.

• International Journal of Fluid Machinery and Systems
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• 제5권4호
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• pp.143-151
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• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제53권6호
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• pp.324-329
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• 2004

Six stator slot couplers(SSC) and a flux probe sensor installed on the stator winding slots of large turbine generator. Assessment of insulation condition has been based upon the measurements of partial discharge(PD) of stator windings and shorted-turn of rotor windings in operating large turbine generator. The maximum PD magnitude(Qm), normalized quantify number(NQN), PD pattern and shorted-turn were measured using on-line insulation condition monitoring system. The NQN and Qm of slot PD side in the phase A are indicated the highest value in six SSC sensors. Monitoring system results showed that discharge at conductor surface and internal discharge were detected at the surface of stator winding and in voids of the groundwall insulation. Insulation of stator and rotor windings in large turbine generator was judged to be in good condition.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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• pp.500-501
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• 2007

Automation systems for higher performance and efficiency in railway electrification systems are driving ever more demanding needs for new condition monitering systems which can consist of sensors connected to the substation and catenary systems. This paper reviews the recently developed condition monitering system, based on a IP network-based multi-agent system, wireless communications and sensor networks for railway electrification system. A new concept for information management, condition monitoring and control of power transmission are considered as railway automation in electrification system.

• International Journal of Aeronautical and Space Sciences
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• 제9권2호
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Corrosion Science and Technology
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• 제15권6호
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• pp.271-280
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• 2016

An approach to achieving the ambitious goal of cost effectively extending the safe operation life of energy pipeline to 100 years is the application of health monitoring and life prediction tools that are able to provide both long-term remnant pipeline life prediction and in-situ pipeline condition monitoring. A critical step is the enhancement of technological capabilities that are required for understanding and quantifying the effects of key factors influencing buried steel pipeline corrosion and environmentally assisted materials degradation, and the development of condition monitoring technologies that are able to provide in-situ monitoring and site-specific warning of pipeline damage. This paper provides an overview of our current research aimed at developing new sensors and electrochemical cells for monitoring, categorising and quantifying the level and nature of external pipeline and coating damages under the combined effects of various inter-related variables and processes such as localised corrosion, coating cracking and disbondment, cathodic shielding, transit loss of cathodic protection.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.