• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.161-169
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• 2023

Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.1
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• pp.6-10
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• 2023

Only about 10% of selected equipment in nuclear power plants are monitored by wiring to address failures or problems caused by vibration. The purpose is primarily for preventive maintenance, not for predictive maintenance. This paper shows that vibration monitoring and diagnosis using Industrial 4.0 enables the complete predictive maintenance for all vibrating equipments in nuclear power plants with the convergence of internet of things; wireless technology, big data through periodic collection and artificial intelligence. Predictive maintenance using wireless technology is possible in all areas of nuclear power plants and in all systems, but it should satisfy regulatory guides on electromagnetic interference and cyber security.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.3
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• pp.91-100
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• 2017

The solution was developed for the maintenance decision support of combined cycle power plant gas turbine. The developed solution was applied to MHI501G gas turbine and is, in present, on the process of field test at GUNSAN combined cycle power plant, South Korea. The developed solution provides the calculated result of optimal overhaul maintenance period through following modules: Real Time Performance Monitoring, Model-Based Diagnostics, Performance Trend Analysis, Optimal Overhaul Maintenance Interval, Compressor Washing Period Management, and Blade Path Temperature Analysis. Model-Based Diagnostics module analyzed the differences between the data of gas turbine performance model and the online measurement. Compressor washing management module suggests the optimal point of balancing between the compressor performance and the maintenance cost.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.503-510
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• 2024

This study investigated the extent to which sensor data has been utilized for the application of CBM+ (Condition-Based Maintenance Plus) technology in major weapon systems of the Korean military. Based on the survey results, the level of each military branch was analyzed using evaluation metrics from the perspective of the data lifecycle. Additionally, key considerations for applying CBM+ technology to operational weapon systems were presented.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.355-362
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• 2012

Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.329-336
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• 2008

The facilities with new technologies in the recent power plants become larger and need a lot of high cost for maintenance due to stop operations and accidents from the operating machines. Therefore, it claims new systems to monitor the operating status and predict the faults of the machines. This research classifies the normal/abnormal status of the machines into 5 levels which are normal-level/abnormal-level/care-level/dangerous-level/fault and develops the new system that predicts faults without stop operation in power plants. We propose the regional segmentation technique in the frequency domain from the data of the operating machines and generate the Peak-codes similar to the Bar-codes, The high efficient and economic operations of the power plants will be achieved by carrying out the pre-maintenance at the care level of 5 levels in the plants. In order to be utilized easily at power plants, we developed the algorithm appling to a notebook computer from signal acquisition to the discrimination.

• Journal of Navigation and Port Research
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• v.26 no.3
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• pp.269-274
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• 2002

It is said to be very difficult to maintain the aids to navigation such as buoys end lighthouses due to their geographical characteristics. This paper, as a part task of the construction of aids to navigation control center, describes the method to make the module for remote-controlling lighthouses and buoys. We become to be able nor only to get informations such as the condition of lights and batteries, but also to control remotely the aids to navigation by using microwaves so as to maintain them in good condition.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.11-16
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• 2022

As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.9
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• pp.665-677
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• 2019

The United States has implemented the TLCSM (Total Life Cycle System Management) to minimize the total lifecycle cost of aircraft and to improve operating availability. As a practical strategy, CBM + is required to be applied to new weapons systems. The F-35 aircraft applied PHM under CBM + concept from the development stage. In this study, we analyzed the technology trends, the level of PHM technology in Korea, and the development trends of foreign technology. Then, we analyzed the PHM technical elements and constructed the 5 phases of technical elements acquisition roadmap for military fixed wing aircraft parts PHM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.5899-5905
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• 2014

Faults in electric power system can be a critical problem for vehicles. The system durability is determined mainly by the durability of their components and operating conditions. Monitoring the conditions of the electric power system may be necessary because it is very difficult to predict precisely when it will fail. Therefore, the aim of this study was to develop a diagnosis system for an electric power system of a vehicle. The alternator voltage, excitation voltage, lamp voltage, battery voltage, and engine rpm from a crank angle sensor are monitored continuously and the system fault can be then detected in real time. NI USB- 9201 DAQ and LabVIEW SW have been used to measure the voltages and analyze the data. Compared to conventional measurements for only each component, an integrated and portable measurement method was developed. In addition to the monitoring the electric power system in real time, the saved data from the measurement also provides valuable information to improve the durability of the components.