• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.416-423
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• 2003

During the last ten years, the need has been increased for reducing maintenance cost for aged equipments and ensuring safety, efficiency and profitability of petrochemical and refinery plants. RBI (Risk Based Inspection) methodology is one of the most promising technologies satisfying the need in the field of integrity management. In this study, a user-friendly software, realRBl for RBI based on the API 581 code was developed. This software has modules for evaluating qualitative and semi-quantitative risk level, analyzing quantitative risks using the potential consequences of a failure of the pressure boundary, and assessing the likelihood of failure. A quantitative analysis was performed for 16 columns in a domestic NCC (Naphtha Cracking Center) plant whose operating time reaches about 12 years. Each column was considered as two equipment parts by dividing into top and bottom. Generic column failure frequencies were adjusted based on likelihood data. After determining release rate, release duration and release mass for each failure scenario, flammable/explosive and toxic consequences were assessed. Current risks for 32 equipment parts were evaluated and risk based prioritization were determined as a final result.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.121-130
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• 2008

Recently, we can see an increasing amount of dam damage or failure due to aging, earthquakes occurrence and unusual changes in weather. For this reason, dam safety is gaining more importance than ever before in terms of disaster management at a national level. Therefore, the government is trying to come up with an array of legal actions to secure consistent dam safety. Other dam management organizations are also taking various institutional and technical measures for the same purpose. In this study, Dam Safety Management System, KDSMS, has developed for consistent and efficient dam safety management. The KDSMS consists of dam and reservoir data, a hydrological information system, a field inspection and data management system, a instrumentation and monitoring system including earthquake monitoring, a field investigation and safety evaluation system, and a collective information system. The KDSMS is a kind of enterprise management system which has been developed to deal with safety management of each field, research center, and headquarter office and their correlation as well as detailed safety information management.

• Smart Structures and Systems
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• v.5 no.2
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• pp.173-191
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• 2009

Civil infrastructures are always subjected to various types of hazard and deterioration. These conditions require systematic efforts to assess the exposure and vulnerability of infrastructure, as well as producing strategic countermeasures to reduce the risks. This paper describes the needs for and concept of advanced sensor technologies for risk assessment of civil infrastructure in Japan. Backgrounds of the infrastructure problems such as natural disasters, difficult environment, limited resource for maintenance, and increasing requirement for safety are discussed. The paper presents a concept of risk assessment, which is defined as a combination of hazard and structural vulnerability assessment. An overview of current practices and research activities toward implementing the concept is presented. This includes implementation of structural health monitoring (SHM) systems for environment and natural disaster prevention, improvement of stock management, and prevention of structural failure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.135-142
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• 2006

Engineers are always concerned with life cycle costs for making important economic decisions through engineering action like reliability of products. Decisions during the reliability growth development of products involve trade-offs between invested costs and its returns. In order to find minimal LCC containing the reliability improvement cost, production cost, repair and replacement costs, and holding cost of spare parts for failure items we suggest in this paper relationship between development cost and sustaining cost in values of growth parameter $\beta$ of AMSAA model. This model is applied to the reliability growth program based on AMSAA model during R&D phase, the warranty activities of items and the block replacement policy for maintenance of items in avionic equipment.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.19-29
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• 2007

This study deals with developing and operating Reliability Analysis & Evaluation System for aircraft parts by analyzing ROKAF's AMMIS and phase inspection data for optimal inspection frequency. We suggest operating model with improving and adjusting inspection cycle by analyzing failure time data and tendency of crack with RELEX and Minitab software.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.20-23
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• 2007

A generalized spare ordering policy is treated in this paper. If the operating unit fails before a scheduled ordering time an expedited order is placed at the failure time instant, otherwise a regular order for a spare is placed at the scheduled time. The original unit is replaced when the ordered spare is delivered. The lifetime, regular and expedited lead times have general distributions. The problem is to find the optimum ordering time which minimizes the expected cost rate including the observation, ordering, uptime and down-time costs. Some properties regarding the optimal policy are derived. To explain the spare ordering policy a numerical example is also included.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.4
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• pp.202-208
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• 2005

In Korea it tends to rely on foreign standards for the stability management of the embankment slope on the soft clay layer. The Matsuo-Kawamura's method, the Kurihara's method, the Tominaga- Hashimoto's method and the Shibata-Sekiguchi's method are generally employed at site. In this study these slope stability methods are investigated and the applicability of the stability management methods is evaluated through numerical analysis. It is evaluated that stability is overestimated to some degree by the Matsuo-Kawamura method. According to the result by the Tominaga-Hashimoto method there is some risk of sudden failure. This implies that the careful attention is necessary for the management of monitoring the field data. Even though the stability tends to be underestimated by the Kurihara's method, however, it is estimated that this method is applicable to the field when the probable uncertainty at site is considered. For the Shibata-Sekiguchi's method, there is some difficulties in determining the failure index for the practical application, it is considered as safe when the existing estimated failure index is greater than ${\Delta}_q/{\Delta}{\delta}$. In this study, however, it is evaluated to be safe as well when ${\Delta}_q/{\Delta}{\delta}$ to load shows the tendency of constant increase.