• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.4
• /
• pp.71-82
• /
• 2021

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.569-570
• /
• 2022

Failure situations occur frequently in the industry, and the existing 2D-based manual for this purpose is not intuitive to understand and it is difficult to immediately interact with maintenance experts. In this paper, we propose a technology that enables workers to perform maintenance in real time with the help of experts without restrictions in time and place based on augmented reality when a failure situation occurs at a remote location. A local virtual human-based expert diagnoses a failure situation based on an adapted panoramic image of a remote failure situation while wearing an HMD headset, and gives instructions to a remote operator. In addition, in an augmented reality (AR) environment in a remote location, for the interaction between the operator and the expert, the HMD's microphone is used to create the expert's hand as well as verbal communication. You can use to point or draw a picture. If this technology is used, it is possible to overcome the limitations of the existing 2D-based manual, and to provide assistance in performing maintenance smoothly remotely even if an expert does not directly visit a remote location.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.40 no.9
• /
• pp.835-842
• /
• 2016

In Korea, urban railway cars are typically maintained using the strategy of predictive maintenance. In an effort to overcome the limitations of the existing strategy, there is increased interest in adopting the condition-based maintenance strategy. In this study, a novel method is proposed to detect faults in the solenoid valves of the braking system in urban railway vehicles. We determined the key component (i.e., solenoid valve) that leads to braking system faults through the analysis of failure modes, effects, and criticality. Then, an equivalent circuit model was developed with the compensation of the temperature effect on solenoid coils. Finally, we presented how to detect faults with the equivalent circuit model and current signal measurements. To demonstrate the performance of the proposed method, we conducted a case study using real solenoid valves taken from urban railway vehicles. In summary, it was shown that the proposed method can be effective to detect faults in solenoid valves. We anticipate the outcome from this study can help secure the safety and reliability of urban railway vehicles.

• Korean Journal of Construction Engineering and Management
• /
• v.19 no.6
• /
• pp.86-93
• /
• 2018

Infrastructure which mandatory in human life causes large environmental loads when they are being installed and maintained. Especially, maintenance is performed over a long period of time. Also, there is a limit to suggest a reliable estimated value because environmental loads are changed according to methods of maintenance and periods. In this study, we developed a Environmental Load Estimating Model to evaluate value and plan as soon as possible in the Early Design Phases while maintaining a tunnel. To estimate environmental loads by using brief design information, we analyze a calculation methodology of environmental loads in maintenance phases. Furthermore, we apply periods of maintenance work and maintenance factors considered a characteristic of long-term maintenance. Finally, a main purpose is that this program makes all users estimate environmental loads in maintenance phases easily and quickly. Accordingly, it is considered that the Environmental Load Estimating Model offer assistance to eco-friendly maintenance of the road and tunnel construction.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.55-55
• /
• 2023

물 인프라 시설물은 다양한 산업 및 지역 사회에 필요한 물 공급과 이와 관련된 인프라를 제공하는 현대 사회의 중요한 구성 요소 중 하나이다. 이러한 시설이 노후화 되면서 안전과 신뢰성에 대한 우려가 커지면서 과거 건설, 개발 중심에서 유지, 관리 중심으로 패러다임이 변화하고 있다. 이에 물 인프라 시설물의 상태를 정밀하게 조사하고, 정확한 계측하는 기술에 대한 수요가 지속적으로 증가하고 있다. 최근, 드론에 초분광 센서를 탑재하여 초분광 영상을 수집할 수 있는 기술이 개발되고 있으며, 물 인프라 시설물에 대한 원격탐사 및 모니터링이 이러한 수요를 만족시킬 수 있는 유망한 해결책으로 부상하고 있다. 특히, 이러한 초분광 영상 수집 기술을 이용하면 물 인프라 시설물 주변의수심, 수질, 온도 등 환경적 요인 뿐만 아니라 재료에 따른 상태를 파악할 수 있어, 잠재적으로 구조 결함을 감지하는데 필요한 상세한 분광 정보를 수집할 수 있다. 또한, "저수지·댐의 안전관리 및 재해예방에 관한 법률"에서 정기적인 정밀안전진단을 요구하고 있으며, "중대재해처벌법"에 따라 인력중심의 조사, 계측 방식의 어려움이 있는 상황에서 드론 기반의 초분광 원격탐사 기술은 매력적인 선택지이다. 본 연구는 안전과 신뢰성에 대한 우려가 커지고 있는 물 인프라 시설물에서 드론 기반 초분광영상 기술이 제공하는 새로운 해결책에 대한 연구로, "저수지·댐 안전관리 및 재해예방에 관한 법률"에서 제시하는 물 인프라 시설의 정기적인 검사 및 유지보수에 대한 중요성을 더욱 강조하는 것으로, 물 인프라 시설물을 정확하게 모니터링하고 조사, 계측하는 능력의 중요성을 증가시킬 수 있는 기술이다. 따라서 본 연구에서는 드론 기반의 초분광 영상 수집 기술을 활용하여 물 인프라시설물의 원격탐사 및 모니터링에 대한 적용성을 검토하고자 한다. 이를 통해 드론 기반 초분광영상 기술이 물 인프라 시설물의 조사 및 계측, 유지 보수에 대한 새로운 해결책이 될 수 있는지 검토한다. 또한 이 기술의 잠재적인 이점과 한계를 탐구하고, 정확하고 신뢰성 높은 계측, 조사 기술에 대한 증가하는 수요를 충족시키기 위한 능력을 평가 하고자 한다.

• Journal of Korean Society of Disaster and Security
• /
• v.5 no.1
• /
• pp.43-47
• /
• 2012

The asset management of infra facilities is a total framework for finally supporting a safe and comfortable service, which includes functions of supporting evaluation of condition and performance of infrastructures, making the decision method of repair or rehabilitation of deteriorated facilities, and lengthening the life cycle of structure through the decision of adequate cost and time of repair or reinforcement. In the range of the asset management, organization, human, the target, and information & data of company are included. Therefore, in this paper, appling the method of asset management analysis to the infra structures, the process of the risk assesment using BRE (Business Risk Exposure) and the basis of consisting ORDM (Optimized Renewal Decision-Making) are expressed.

• Proceedings of KOSOMES biannual meeting
• /
• 2017.04a
• /
• pp.51-51
• /
• 2017

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.75-84
• /
• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2008.11a
• /
• pp.149-154
• /
• 2008

Service life of bridges should be evaluated by physical life considering damage/deterioration. But it is difficult to identify optimal maintenance scenario due to insufficient research related to that. To identify optimal maintenance scenario, it is needed to develope life cycle profile model of condition state variation by deterioration factor. The LCP model has been developed in consideration of regression analysis and survey in this study. It is expected that the LCP model could help to achieve HBMS system improvement.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.5
• /
• pp.89-94
• /
• 2021

Recently, manufacturing companies are introducing many intelligent production processes that apply IIoT/ICT to improve competitiveness, and a system that maintains availability, improves productivity, and optimizes management costs is needed as a preventive measure using environmental data generated from air ejectors. Therefore, in this study, a dedicated control board was developed and LoRa communication module was applied to remotely control it to collect and manage information about compressors from cloud servers and to ensure that all operators and administrators utilize common data in real time. This dramatically reduced M/S steps, increased system operational availability, and reduced local server operational burden. It dramatically reduced maintenance latency by sharing system failure conditions and dramatically improved cost and space problems by providing real-time status detection through wired and mobile utilization by maintenance personnel.