• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.11b
• /
• pp.270-273
• /
• 2001

Electrical tree is studied widely by manufacture state of insulating material fare and blazing fire diagnosis system of use in phenomenon of part discharge that happen for main cause of dielectric breakdown of equipment for electric power. Use process that draw tree pattern here measuring above zero to study special quality of this electricity tree, real-time processing by image processing is proposed because reproduction of tree blazing fire process drops and pattern of tree is difficult correct quantification of tree growth by existent visual observation by involution. This research presents general process that need in image processing of tree blazing fire, and that remove various noises that happen in above zero by measuring electrical tree dividing background and complete view in measured above zero taking advantage of specially proposed complete view object abstraction techniques effectively and quantification of tree becomes easy naturally, can apply to dielectric breakdown estimate because can chase growth process of tree.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.12
• /
• pp.5307-5327
• /
• 2016

Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we propose a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN. First, we develop two different types of charging cycles: initialization cycles and renewable cycles and give relevant algorithms to construct these two cycles for each sensor node. We then formulate the optimization problem into an optimal construction algorithm and prove its correctness through theoretical analysis. Finally, we conduct extensive simulations to demonstrate the effectiveness of our proposed algorithms.

• Proceedings of the KIEE Conference
• /
• 2004.11a
• /
• pp.251-254
• /
• 2004

The aging diagnosis technique using partial discharge detection method detects partial discharge signals cause of power equipment failuer and able to forecast the aging state of insulation system through analysis algorithm, in this paper accumulates HFPD signal during constant scheduled cycles to build HFPD pattern and then analyzes HFPD pattern using statistical parameters and ${\Delta}F$ pattern. The 3D pattern is composed of detected signal frequency, amplitude and repeated number and the FRPDA(frequency resolved partial discharge analysis) technique is used in 3D pattern construction. The ${\Delta}F$ pattern shows variation characteristics of amplitude gradient of consecutive HFPD signal Pulses and able to classify discharge types-internal discharge, surface discharge and coronal discharge etc. Fractal mathematics applied to ${\Delta}F$ pattern quantification and neural networks is used in aging diagnostic algorithm.

• Tribology and Lubricants
• /
• v.36 no.6
• /
• pp.307-314
• /
• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• Journal of Sensor Science and Technology
• /
• v.32 no.3
• /
• pp.199-206
• /
• 2023

This study presents a novel monitoring technique for underwater high-voltage direct current (HVDC) cables based on the Distributed Acoustic Sensor (DAS). The proposed technique utilizes vibration and acoustic signals generated on HVDC cables to monitor their condition and detect events such as earthquakes, shipments, tidal currents, and construction activities. To implement the monitoring system, a DAS based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) system was designed, fabricated, and validated for performance. For the HVDC cable monitoring experiments, a testbed was constructed on land, mimicking the cable burial method and protective equipment used underwater. Defined various scenarios that could cause cable damage and conducted experiments accordingly. The developed DAS system achieved a maximum measurement distance of 50 km, a distance measurement interval of 2 m, and a measurement repetition rate of 1 kHz. Extensive experiments conducted on HVDC cables and protective facilities demonstrated the practical potential of the DAS system for monitoring underwater and underground areas.

• Journal of Drive and Control
• /
• v.20 no.4
• /
• pp.17-26
• /
• 2023

A forklift is an industrial vehicle that lifts or transports heavy objects using a hydraulically operated fork, and is equipped with an automatic transmission for the convenience of repetitive transportation, loading, and unloading work. The Transmission Control Unit (TCU) is a key component in charge of the shift control function of an automatic transmission. It consists of an electric circuit with an input/output signal interface function and firmware running on a microcontroller. To develop TCU firmware, the development process of shifting algorithm design, firmware programming, verification test, and performance improvement must be repeated. A simulator is a device that simulates a mechanical system having dynamic characteristics in real time and simulates various sensor signals installed in the system. The embedded transmission simulator is a simulator that is embedded in the TCU firmware. information related to the mechanical system that is necessary for TCU normal operation. In this study, an embedded transmission simulator applied to the originally developed forklift TCU firmware was designed and used to verify various forklift shift control algorithms.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.2
• /
• pp.20-27
• /
• 2007

[ $SF_6$ ] gas for compacted power facilities has a important role as an insulation gas. It is very blown well that $SF_6$ gas has the superior characteristics as an insulation gas. For reliable operation of SF6-gas-based high and medium voltage equipment it is very important to keep the insulation ability within a safe range. And the experimental and measuring system were implemented. The test chamber designed to endure up to 3 atmospheric pressure. The analysis results of the experimental data shows that positive partial discharge can be detected by discharge current and UHF signal. Additionally it is shown the possibility that $CO_2$ gas sensor of semiconductor type can be detect the variation of $SF_6$ gas condition. The UHF sensor shows good feature to detect the variation of $SF_6$ gas condition for partial discharge and breakdown discharge.

• Journal of Convergence for Information Technology
• /
• v.11 no.12
• /
• pp.30-38
• /
• 2021

In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.

• Journal of the Society of Naval Architects of Korea
• /
• v.60 no.2
• /
• pp.95-109
• /
• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of Radiation Industry
• /
• v.17 no.3
• /
• pp.265-273
• /
• 2023

To effectively and safely manage the radiation exposure to nuclear power plant (NPP) workers in accidents, major overseas NPP operators such as the United States, Germany, and France have developed and applied realistic 3D model radiation dose assessment software for workers. Continuous research and development have recently been conducted, such as performing NPP accident management using 3D-VR based on As Low As Reasonably Achievable (ALARA) planning tool. In line with this global trend, it is also required to secure technology to manage radiation exposure of workers in Korea efficiently. Therefore, in this paper, it is described the application method and assessment results of radiation exposure scenarios for workers in response to accidents assessment technology, which is one of the fundamental technologies for constructing a realistic platform to be utilized for radiation exposure prediction, diagnosis, management, and training simulations following accidents. First, the post-accident sampling after the Loss of Coolant Accident(LOCA) was selected as the accident and response scenario, and the assessment area related to this work was established. Subsequently, the structures within the assessment area were modeled using MCNP, and the radiation source of the equipment was inputted. Based on this, the radiation dose distribution in the assessment area was assessed. Afterward, considering the three principles of external radiation protection (time, distance, and shielding) detailed work scenarios were developed by varying the number of workers, the presence or absence of a shield, and the location of the shield. The radiation exposure doses received by workers were compared and analyzed for each scenario, and based on the results, the optimal accident response scenario was derived. The results of this study plan to be utilized as a fundamental technology to ensure the safety of workers through simulations targeting various reactor types and accident response scenarios in the future. Furthermore, it is expected to secure the possibility of developing a data-based ALARA decision support system for predicting radiation exposure dose at NPP sites.