• Journal of the Society of Naval Architects of Korea
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• v.55 no.6
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• pp.505-513
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• 2018

Main engine failures in ship operations can lead to a major damage in terms of the vessel itself and the financial cost. In this respect, monitoring of a vessel's main engine condition is crucial in ensuring the vessel's performance and reducing the maintenance cost. The collection of a huge amount of vessel operational data in the maritime industry has never been easier with the advent of advanced data collection technologies. Real-time monitoring of the condition of a vessel's main engine has a potential to create significant value in maritime industry. This study presents a case study on the establishment of upper control limit to detect vessel's main engine failures using multivariate control chart. The case study uses sample data of an ocean-going vessel operated by a major marine services company in Korea, collected in the period of 2016.05-2016.07. This study first reviews various main engine-related variables that are considered to affect the condition of the main engine, and then attempts to detect abnormalities and their patterns via multivariate control charts. This study is expected to help to enhance the vessel's availability and provide a basis for a condition-based maintenance that can support proactive management of vessel's main engine in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.515-528
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• 2020

Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.105-119
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• 2015

The overseas expansion is essential to expand domestic geospatial industries in a state of saturation. But current overseas expansion method has be limited to expand global market. Inter-industry convergence strategies may be the most resonable alternative to expand global market through raising the expansion possibility to developing countries with ODA funds and to developed countries with converging global competitive industries. This research investigates various foreign developed and developing countries to draw each demand. As a result, easiness of convergence, confidentiality of information, complementarity of poor infrastructure, responsiveness of various demands and sustainability of system are needed to successful convergence on multiple industries. This research seeks convergence framework to meet this demands, and suggests each component. This convergence framework is consisted of geospatial convergence common framework, inter-industry convergence model and institutional supporting system for overseas expansion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1949-1954
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• 2016

Smart grid technology can be called grid techniques to improve the efficiency of the electric power by exchanging bidirectional information of electric power with real-time between electric power suppliers and consumers. Recently, the solar power generation system is being applied actively. However the solar power system has several problems leading to reduce overall electricity generation, because the difficult of the diagnosis and the solar power system failure such as PV(PhotoVoltaics) and inverter. In order to build an efficient smart grid, a stable electric power energy requirements capture and management and early fault detection is essentially required in solar power generation system. In this paper, it is designed to monitor the operating status of the solar power monitoring system from a remote location through a RS-485 or TCP/IP communication module to monitoring the output of solar power energy and abnormal phenomenon, to developing the measurement module and to transfer measured data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.364-366
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• 2017

Unlike general ships, offshore plants require high reliability due to their long operating time at fixed positions when they are operated. Sensor-based status information is required for user and maintenance worker to ensure safety. In this paper, we propose a monitoring system for safety diagnosis and inspection of cargo lift for offshore plant. It consists of a sensor unit mounted on the cargo Lift, an embedded system measurement unit, and a monitoring unit for real-time data verification. It is based on the ship standard network IEC 61162-450 for the exchange of operating information and sensor measurement information in accordance with the upgrading and integration of equipment in maritime.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.429-430
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• 2008

본 논문은 전기출력이 1400 MWe급으로 개발된 첨단 원자력 발전소인 APR1400(신형겨수로 1400) 제어계통에 적용되는 디지털시스템의 설계 및 성능 검증을 위해 개발 중인 디지털제어계통 검증시스템에 관한 것이다. APR1400 디지털제어계통은 발전소 출력 제어 및 안전운전과 관련 된 중요 기능들을 수행하며, 기존 원자력발전소와 달리 단일 디지털 Platform을 적용하고, Multi-Loop 개념과 네트워크을 적용하여 Controller와 케이블 수량을 줄인 특징을 가지고 있다. 이와 같을 설계는 지금가지 원자력발전소에는 적용된 적이 없기 때문에 사용자 측면에서는 디지털 제어 계통 설계 및 성능 관점에서의 검증을 위한 시스템이 요구되었다. 현재는 APR1400 시뮬레이터(발전소 모델링을 통한 모의시스템)를 이용한 검증시스템을 1차적으로 구축한 상태에 있으며, 시스템 전체 시험을 진행 중에 있다. 특히, 이번에 개발 중인 검증시스템은 구성이 간단하고 사용이 편리한 장점을 지니고 있을 뿐만 아니라 다양한 고장상황을 재현해 봄으로써 디지털제어계통의 성능을 확인해 볼 수 있는 특징을 보유하고 있다. 본 검증시스템의 활용방안으로는 첫째, 계통설계의 구현 가능성 관점에서의 확인시험을 수행하는 방안, 둘째, 발전소 시운전 착수 전 시운전요원 교육에 활용하는 방안, 셋째, 발전소 설계 변경 필요 시 설계 변경에 따른 영향 파악, 넷째, 디지털제어계통 유지보수 기술 습득 등에 효과적으로 활용 할 수 있을 것으로 본다. AFR1400 디지털제어계통은 현재 건설 중인 신고리 3,4호기 원자력발전소에 적용될 예정이며, 향후에는 해외 원자력 수출을 위한 기반기술로 활용될 수 있을 것으로 확신한다.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1163-1170
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• 2010

In this paper, in order to develop a remote progress management system through the Internet, we show a design method to make easy maintenance by developing the system with both the JAVA language and GoF Design Patterns. For the system implementation, we added the RS232C and RS422/RS485 communication modules to PLC(Programmable Logic Controller) in the control box which provide the real time status data of machines. Also we set up the RS232C to Ethernet converter based on wireless environment to communicate the PLC control data. We use JAVA Swing components to implement the multi-tier architecture system supported the GUI of the Applet and Frame at the same time so that the manager grasps the progress of work easily at the remote machines through the Internet. The key objective of the multi-tier architecture is to share resources among clients, this proposed system can help to develop the software to control the remote machine, and also it has the advantage that developer who wants to make a similar software can make easy to add new function reusing the existing codes.