• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2009.10a
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• pp.236-239
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• 2009

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.27-33
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• 2013

Nowadays, the utilization of renewable energy sources like wind energy is considered one of the most effective means of generating massive amounts of electricity. This is evident in the rapid increase of wind farms all over the world which comprise a huge number of wind turbines. However, the drawback of utilizing wind turbines is that it requires maintenance, which could be a costly operation. To keep the wind turbines in pristine condition so as to reduce downtime, the implementation of CMS (Condition Monitoring System) and FDS (Fault Detection System) is mandatory. The efficiency and accuracy of these systems are crucial in deciding when to carry out a maintenance process. In this paper, a fault detection system based on intelligent clustering method is proposed. Using SCADA data, the clustering model was trained and evaluated for its accuracy through rigorous simulations. Results show that the proposed approach is able to accurately detect the deteriorating condition of a wind turbine as it nears a downtime period.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.63-68
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• 2016

Environmental downtime produces a significant cost to organizations and makes them unable to do business because what happens in the data center affects everyone. In addition, the amount of electrical energy consumed by data centers increases with the amount of computing power installed. Installation of physical Information Technology and facilities related to environmental concerns, such as monitoring temperature, humidity, power, flood, smoke, air flow, and room entry, is the most proactive way to reduce the unnecessary costs of expensive hardware replacement or unplanned downtime and decrease energy consumed by servers. In this paper, we present remote system for monitoring datacenter implementing using open-source hardware platforms; Arduino, Raspberry Pi, and the Gobetwino. The sensed data displayed through Arduino are transferred using Gobetwino to the nearest host server such as temperature, humidity and distance every time an object hitting another object or a person coming in entrance. The raspberry Pi records the sensed data at the remote location. The objective of collecting temperature and humidity data allows monitoring of the server's health and getting alerts if things start to go wrong. When the temperature hits $50^{\circ}C$, the supervisor at remote headquarters would get a SMS, and then they would take appropriate actions to reduce electrical costs and preserve functionality of servers in data centers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.5
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• pp.39-45
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• 2013

Wind power is one of the fastest growing renewable energy sources. In these days, wind turbine shifts from onshore to offshore because the offshore wind farm has a abundant wind resource. However, offshore wind turbine is not easy to access, it has a long downtime when the failures of the wind turbine component occur. Therefore, the appropriate wind turbine maintenance plan is required to meet the economic and reliability of the components. This paper proposes the maintenance planning method based on the RCM(Reliability Centered Maintenance) to determine an economical maintenance cycle to satisfy the appropriate reliability of the wind turbine components. In order to compare the proposed method with the conventional RCM method, critical components are selected in the case study because they have a long downtime and a large amount of total cost.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.75-86
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• 2019

Purpose: It is essential for the steel industry to produce steel products without unexpected downtime to reduce costs and produce high quality products. A hot strip rolling mill consists of many mechanical and electrical units. In condition monitoring and diagnosis, various units could fail for unknown reasons. Methods: In this study, we propose an effective method to detect units with abnormal status early to minimize system downtime. The early warning problem with various units was first defined. An autoencoder was modeled to detect abnormal states. An application of the proposed method was also implemented in a simulated field-data analysis. Results: We can compare images of original data and reconstructed images, as well as visually identify differences between original and reconstruction images. We confirmed that normal and abnormal states can be distinguished by reconstruction error of autoencoder. Experimental results show the possibility of prediction due to the increase of reconstruction error from just before equipment failure. Conclusion: In this paper, hot strip roughing mill monitoring method using autoencoder is proposed and experiments are performed to study the benefit of the autoencoder.

• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.87-92
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• 2023

The number of unmanned light rail train operators is continuously increasing in Korea. In a failure event during an operation due to the nature of the unmanned operation, recovery is performed based on the remote control. However, if remote recovery is not feasible, safety personnel arrive at the train to resume the train operation. There are regulations on safety personnel and the suspension time of the train operation. However, there is currently no rule for safety personnel deployment. Currently, railway operating organizations operate in three scenarios: safety personnel on board trains, stationed at stations, and deployed at major stations. Four major factors influence the downtime for each emergency response scenario. However, these four influencing factors vary too much to predict results with simple calculations. In this study, four influencing factors were considered as random variables with high uncertainty. In addition, the Monte Carlo method was applied to each scenario for the safety personnel deployment to predict train service downtime. This study found a 17% difference in train service suspension by safety personnel deployment scenario. The results of this study can be used in setting service goals, such as standards for future safety personnel placement and frequency of service interruptions.

• Applied Chemistry for Engineering
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• v.31 no.6
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• pp.596-602
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• 2020

Blending process of adhesive production has a cooling process to cool down the temperature of the solution which was heated up to 76 ℃ with a mineral insulated (MI) cable by 30 ℃ at room temperature. Using a MI cable in the adhesive production process makes the production inefficient because it takes about 10 h for the cooling process. If a jacketed vessel is used instead of the MI cable, it would shorten the cooling downtime without any additional cooling system by using cold water. However, there are various types of jacketed vessels, and thus the most suitable type should be found before set up. In this study, we designed the optimized jacketed vessel for the adhesive production process by calculating the cooling downtime, which impacts production efficiency, as a function of the jacket types using computational fluid dynamics. As a result, the cooling performance of the plain jacket was 32.7% superior to that of the half-pipe coil jacket with the same height. In addition, the plain jacket with 60% spiral baffle reduced the cooling downtime and operating time by 80.4% and 25.1%, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.163-168
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• 2022

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.15-23
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• 2006

A spare ordering policy is considered for planned maintenance. Introducing the ordering, uptime, downtime, inventory costs and salvage value, we derive the expected cost effectiveness. The problem is to determine jointly the ordering time for a spare and the preventive replacement time for the operating unit which maximize the expected cost effectiveness. Some properties regarding the optimal policy are derived, and a numerical example is included to explain the proposed model.