• Journal of Information Processing Systems
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• 제2권3호
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• pp.163-169
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• 2006

So far, little research has been conducted into developing a QAPM (Quality Assurance Process Model) for telecommunications applications on the basis of TMN. This is the first trial of the design of TMN-based QAPM on fault management with UML. A key attribute of the QAPM is that it can easily identify current deficiencies in a legacy system on the basis of TMN architecture. Using an empirical comparison with the legacy systems of a common carrier validates the QAPM as the framework for a future mode of the operation process. The results indicate that this paper can be used to build ERP(Enterprise Resource Planning) for a telecommunications fault management solution that is one of the network management application building blocks. The future work of this paper will involve applying the QAPM to build ERP for RTE (Real Time Enterprise) fault management solution and more research on ERP design will be necessary to accomplish software reuse.

• 경영정보학연구
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• 제12권3호
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• pp.1-17
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• 2010

글로벌 싱글 인스턴스(GSI) 기반의 기업의 정보시스템은 기업 내부정보 및 자원/자산을 통합관리하고 프로세스의 동질성을 확보하여 업무의 효율성 및 전사적인 생산성을 향상시키고 있지만 정보시스템의 장애가 발생하게 되면 비즈니스가 마비될 수 있고 그로 인해 엄청난 금전적 손실이 따르는 위험을 동시에 안고 있다. 여분의 부품 사용을 전제하는 수많은 결함허용(Fault-Tolerance) 기법들이 안정적인 정보시스템을 위해 연구되어 왔다. 결함허용 기법의 설계 및 적용의 어려운 점은 결함의 유형 및 빈도를 정보시스템을 운영하기 전에는 알기 어렵다는 것이다. 따라서 정보시스템 구축 단계에서의 결함허용 기법의 적용과 함께, 구축 후, 운영적인 측면에서의 장애관리 기법을 동시에 고려하여 안정적으로 정보시스템을 운영하는 것이 매우 중요하다. 본 연구에서는 기술분류체계(TRM)기반의 점검포인트와 이벤트 룰엔진 적용으로 시스템 장애를 사전예측 하는 방법을 포함하는 정보시스템 장애관리 기법인 무장애체계 프로세스를 제시 하였다. 또한, 무장애체계 방법을 적용한 장애관리 정보시스템(PICS)을 하이테크 장치산업의 대표기업에 구축하여 무장애체계 방법 적용 전, 후의 효과도 함께 제시 하였다. 무장애체계 도입 후 월별 장애건수, 장애시간은 각각 46%, 56% 감소하였으며, 장애로 인한 매출에 대한 기회 손실금액은 77% 절감하는 효과를 보였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권4호
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• pp.281-290
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• 2013

IT자산은 조직의 경영목적을 지원해주는 핵심영역이며, IT자산의 장애 발생시 신속한 처리를 지원하는 것은 매우 중요하다. 본 연구에서는 IT자산의 장애가 발생할 경우, 장애해결을 위하여 기존의 장애 데이터를 기초로 장애처리 예측 기법을 제시한다. 제안한 장애처리 예측 기법은 첫째, 기존의 장애처리 데이터를 전처리하여 장애처리 유형별로 분류하고 둘째, 분류된 장애처리 유형과 장애 발생 후 접수된 내용을 키워드 매핑시키는 규칙을 제정하였으며 셋째, 제정된 규칙에 의하여 장애 발생 후 장애처리 방법이 사전에 예측 가능한 기계학습 프로세스를 제시하였다. 제시한 기계학습 프로세스의 유효성을 입증하기 위하여 A사에서 6개월 동안 접수된 33,000여건의 전산기기 장애 데이터를 실험한 결과 장애처리 예측의 적중률이 약 72%였으며, 지속적인 기계학습을 통하여 81%로 향상되었다.

• 산업공학
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• 제25권2호
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• 품질경영학회지
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• 제39권2호
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• pp.234-243
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• 2011

Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• 반도체디스플레이기술학회지
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• 제9권4호
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• 정보처리학회논문지C
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• 제9C권4호
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• pp.497-504
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• 2002

본 논문에서는 시스템의 네트워크 구성 장애를 관리하기 위한 관리 모델과 에이전트들 간의 협력을 통한 장애의 진단 및 복구 알고리즘을 제시하고 있다. 관리 모델에는 장애의 검출, 진단, 복구의 세 단계로 이루어지며 각각은 RBR(Rule-Based Reasoning)에 기반으로 하여 규칙기반 지식 데이터베이스에 있는 규칙을 이용하여 네트워크의 구성 장애를 진단하고 복구한다. 또한 관리 도메인 상의 네트워크에 분포하고 있는 여러 에이전트들 간의 협력을 통하여 시스템 단독으로는 해결할 수 없는 복잡한 문제를 해결하거나 네트워크의 상황까지 고려하여 진단하고 복구함으로써 효율적인 시스템의 네트워크 구성 관리 알고리즘을 제시하고 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.719-722
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• 1998

Fault simulation for large and complex sequential circuits is highly cpu-intensive task in the intergrated circuit design process. In this paper, we propose CM-SIM, a concurrent fault simulator which employs an optimal memory management strategy and simple list operations. CM-SIM removes inefficiencies and uses new dynamic memory management strategies, using contiguous array memory. Consequently, we got improved performance and reduced memory usage in concurrent fault simulation.

• 산업경영시스템학회지
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• 제44권2호
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.