• Journal of Advanced Marine Engineering and Technology
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• v.38 no.4
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• pp.456-462
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• 2014

In this paper, we develop a facility management system which provides fault effect and replacement alarm function of each component for supporting effective maintenance of facility. To do this, we use weighting method to each component, calculate importance of each component, and make them to hierarchy structure using bill of materials of facility. Also, we draw fault cause and fault effect of components based on failure modes effects and criticality analysis, and define criteria of severity, occurrence and detection to get risk priority number. To apply these concepts, we develop and test the facility management system to verify its practicality. In the future, we expect the developed system to apply many domains such as maintenance of ship and offshore plant.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.375-386
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• 2003

A rapid growth of mobile devices and a proliferation of wireless networks lead to changing the existing environment into a mobile environment. Expansion of mobile environment makes a pressing need of mobile databases, and there are several solutions to achieve it. But they still accompany with some problems. Especially, the current synchronization server that manages consistently many mobile database management systems is based on the centralized method to synchronize data between the mobile database systems. It is the major factor that causes the lower performance of the whole system. Furthermore, the fault tolerance has not been considered in the existing systems. Therefore, we suggest the mobile agent-based mobile database system in order to resolve the problems. The system, we propose in this paper, provides the high-performance of the whole system by mobile grouping, and also may support a fault-tolerance management method.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.327-338
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• 2008

A fault tolerant control system design technique has been proposed and analyzed for managing performance degradation in the presence of multiple faults in actuators. The method is based on a control structure with a model reference reconfigurable control design in an inner loop and command input adjustment in an outer loop. The reduced dynamic performance requirements in the presence of different actuator faults are accounted for through different performance reduced (degraded) reference models. The degraded steady-state performances are governed by the reduced levels of command input. The reconfigurable controller is designed on-line automatically in an explicit model reference control framework so that the dynamics of the closed-loop system follow that of the performance reduced reference model under each fault condition. The reduced command input level is determined to prevent potential actuator saturation. The proposed method has been evaluated and analyzed using an aircraft example against actuator faults subject to constraints on the magnitude and slew-rate of actuators.

• Journal of the Korea Safety Management & Science
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• v.4 no.4
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• pp.15-26
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• 2002

The quantitative evaluation method of the safety management cost was suggested to prevent a gas accident as a major industrial accident. In a gas governor station, process risk assessments such as the fault tree analysis(FTA) and the consequence analysis were performed. Based on process risk assessments, potential accident costs were estimated and the cost-benefit analysis(CBA) was performed. From the cost-benefit analysis for five classification items of safety management cost, the order of the cost/benefit ratio was estimated.

• International Journal of Contents
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• v.12 no.1
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.1-6
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• 2006

In this paper, we described the existing methodology of product safety analysis and proposed a Petri-nets based method to analyze product safety systematically. The proposed method can be used to find the defects of hardware/software and the error of human behavior. We also discussed the differences between the Fault Tree Analysis and Petri-nets based method by giving an example.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.459-465
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• 2024

The flight control computer applied to manned/unmanned aircraft is one of the key components directly connected to the control of the aircraft, and is generally designed with a redundant architecture so that essential functions for flight can be maintained even if a failure occurs in a single channel. The operational flight program loaded on these redundant flight control computers should be designed considering a time synchronization between channels, input data selection methods from redundant sensors, and fault detection/isolation methods for channels. In this paper, we propose a redundancy management method applied to triplex compact flight control computers for advanced air vehicle. The proposed redundancy management method includes a synchronization algorithm between triplex channels, an input data voting method from sensors, a bus control right selection method for control command output, and a fault detection/isolation method for channels.

• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.103-112
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• 2001

In Factory, as the number of machine is increased the more maintenance efforts are necessary. Multi maintenance issues may occur at a certain time and the determination of maintenance sequence is needed. In this study, we first compare the priority of machines and the impact value using modified FMEA(Failure Mode Effect and Analysis) method. Also, CBR(Case-based Reasoning) approach is applied to retrieve similar fault cases of current machine problem. The proposed methodology will be useful to implement decision support system of maintenance sequence for CMMS/EAM (Computerized Maintenance Management System/Enterprise Asset Management).

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.287-294
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• 2019

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.45-53
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• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.