• Title/Summary/Keyword: fault prediction

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노후화된 제조공장의 고도화 방법에 관한 연구 (A study on the advanced method of aging manufacturing factory)

  • 김정민;장종욱
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2018년도 추계학술대회
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    • pp.69-71
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    • 2018
  • 대한민국의 제조 산업을 둘러보면 현재 노후화된 제조 공장이 많다. 실제로 제조 공장에서 제품의 재고관리나 제품의 단가 관리 등을 전부 엑셀을 만들어 수기로 작성하거나 그것을 이용하여 공장을 운영 하고 있다. 또한 작업자가 작업 시 해당 제품을 만들기 위해 설비에 대해 고장을 예측 할 수가 없다. 이에 관한 문제점으로 관리자 및 작업자 사이의 지시 및 작업 과정에서 해당 문서가 손실 될 수 있으며 관리자와 작업자의 의사소통이 제대로 되지 않아 재고관리나 해당 지시한 작업에 대해 제대로 작업량을 채우지 못하고 또한 작업자가 해당 설비에 대해 고장을 인지하지 못하고 그 설비를 계속 사용하여 작업을 진행하는 상황이 발생한다. 이에 따라 효율적인 재고관리와 단가관리, 생산량 파악 및 작업자의 해당 설비의 고장예지를 미리 예측하여 제품의 생산성 및 생산량을 높이기 위해 본 연구인 노후화 된 제조공장의 고도화 방법에 관한 연구를 진행 해보자 한다.

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인공신경망을 이용한 수전설비 고장 예측 방법 (A Prediction Scheme for Power Apparatus using Artificial Neural Networks)

  • 기태석;이상호
    • 융합정보논문지
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    • 제7권6호
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    • pp.201-207
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    • 2017
  • 수전설비의 고장은 산업과 가정 등 전력을 사용하는 모든 곳에 정전사고를 발생시켜 많은 불편과 문제의 원인이 되고 있다. 수전설비 고장의 주요 원인으로는 노후화를 비롯하여 태풍, 지진을 비롯한 자연재해와 동물 등으로 파악되고 있다. 현재는 수전설비의 온도가 높아지면 고장이 발생한다는 추측만으로 온도의 고온이 지속되는 것을 모니터링한다. 따라서 적기에 수전설비의 고장에 대응하기 어려운 측면이 존재하는 것이 사실이다. 이 논문에서 제안하는 수전설비 모니터링 시스템은 갑작스런 자연재해 등으로 인한 고장을 제외한 일반적인 고장에 대해 효율적으로 대응하는 방안으로 제안한다. 수전설비 모니터링 시스템은 열 감지 센서를 부착하여 수전설비를 실시간으로 감시하고, 감시한 데이터를 수집하여 인공신경망을 이용한 학습을 통해 축적된 정보를 이용하여 고장을 예측한다. 인공신경망의 학습과 실험을 통하여 제안 방식이 효율적임을 보였다.

피어슨 상관계수를 이용한 적외선 열화상 안전 진단 시스템 개발 (The Development of Infrared Thermal Imaging Safety Diagnosis System Using Pearson's Correlation Coefficient)

  • 정종문;박성훈;이용식;김재현
    • 한국태양에너지학회 논문집
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    • 제39권6호
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    • pp.55-65
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    • 2019
  • With the rapid development of the national industry, the importance of electrical safety was recognized because of a lot of new electrical equipment are installing and the electrical accidents have been occurring annually. Today, the electrical equipments is inspect by using the portable Infrared thermal imaging camera. but the most negative element of using the camera is inspected for only state of heating, the reliable diagnosis is depended with inspector's knowledge, and real-time monitoring is impossible. This paper present the infrared thermal imaging safety diagnosis system. This system is able to monitor in real time, predict the state of fault, and diagnose the state with analysis of thermal and power data. The system consists of a main processor, an infrared camera module, the power data acquisition board, and a server. The diagnostic algorithm is based on a mathematical model designed by analyzing the Pearson's Correlation Coefficient between temperature and power data. To test the prediction algorithm, the simulations were performed by damaging the terminals or cables on the switchboard to generate a large amount of heat. Utilizing these simulations, the developed prediction algorithm was verified.

플라즈마 정보인자를 활용한 SiO2 식각 깊이 가상 계측 모델의 특성 인자 역할 분석 (Role of Features in Plasma Information Based Virtual Metrology (PI-VM) for SiO2 Etching Depth)

  • 장윤창;박설혜;정상민;유상원;김곤호
    • 반도체디스플레이기술학회지
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    • 제18권4호
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    • pp.30-34
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    • 2019
  • We analyzed how the features in plasma information based virtual metrology (PI-VM) for SiO2 etching depth with variation of 5% contribute to the prediction accuracy, which is previously developed by Jang. As a single feature, the explanatory power to the process results is in the order of plasma information about electron energy distribution function (PIEEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable selection (SVS), OES features are selected after PIEEDF. Informative vector for developed PI-VM also shows relatively high correlation between OES features and etching depth. This is because the reaction rate of each chemical species that governs the etching depth can be sensitively monitored when OES features are used with PIEEDF. Securing PIEEDF is important for the development of virtual metrology (VM) for prediction of process results. The role of PIEEDF as an independent feature and the ability to monitor variation of plasma thermal state can make other features in the procedure of SVS more sensitive to the process results. It is expected that fault detection and classification (FDC) can be effectively developed by using the PI-VM.

Precise attitude determination strategy for spacecraft based on information fusion of attitude sensors: Gyros/GPS/Star-sensor

  • Mao, Xinyuan;Du, Xiaojing;Fang, Hui
    • International Journal of Aeronautical and Space Sciences
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    • 제14권1호
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    • pp.91-98
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    • 2013
  • The rigorous requirements of modern spacecraft missions necessitate a precise attitude determination strategy. This paper mainly researches that, based on three space-borne attitude sensors: 3-axis rate gyros, 3-antenna GPS receiver and star-sensor. To obtain global attitude estimation after an information fusion process, a feedback-involved Federated Kalman Filter (FKF), consisting of two subsystem Kalman filters (Gyros/GPS and Gyros/Star-sensor), is established. In these filters, the state equation is implemented according to the spacecraft's kinematic attitude model, while the residual error models of GPS and star-sensor observed attitude are utilized, to establish two observation equations, respectively. Taking the sensors' different update rates into account, these two subsystem filters are conducted under a variable step size state prediction method. To improve the fault tolerant capacity of the attitude determination system, this paper designs malfunction warning factors, based on the principle of ${\chi}^2$ residual verification. Mathematical simulation indicates that the information fusion strategy overwhelms the disadvantages of each sensor, acquiring global attitude estimation with precision at a 2-arcsecs level. Although a subsystem encounters malfunction, FKF still reaches precise and stable accuracy. In this process, malfunction warning factors advice malfunctions correctly and effectively.

비정상 트래픽 분석을 위한 네트워크 모니터링 기반의 네트워크 성능 분석 시스템 (A Network Performance Analysis System based on Network Monitoring for Analyzing Abnormal Traffic)

  • 김시홍;구자환;김승해;최장원;안성진
    • 융합보안논문지
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    • 제4권3호
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    • pp.1-8
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    • 2004
  • Computational Grid나 Data Grid와 같은 대규모 분산 시스템은 장애 탐지, 성능 측정, 성능튜닝, 성능 예측, 스케줄링, 보안 분석 등의 작업을 위해 많은 량의 모니터링 데이터를 필요로 한다. 네트워크 특성을 나타내는 지표들은 네트워크 모니터링 시스템을 통해 얻어질 수 있으며 시스템에 의해 축적된 자료는 상기한 작업의 정확한 분석을 돕는다. 본 논문에서는 네트워크 모니터링 시스템의 한 형태로 네트워크 모니터링기반의 네트워크 성능 및 보안 분석 시스템을 소개한다. 본 시스템은 이용자 중심으로 네트워크 성능 및 보안 데이터를 제공하며, 이용자가 원격지의 네트워크 특성을 측정 환경에 따라 변화해 가며 측정할 수 있게 함으로써 해당 원격지의 네트워크 서비스를 가늠해 볼 수 있게 한다.

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Prediction of Strong Ground Motion in Moderate-Seismicity Regions Using Deterministic Earthquake Scenarios

  • 강태섭
    • 한국지진공학회논문집
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    • 제11권4호
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    • pp.25-31
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    • 2007
  • For areas such as the Korean Peninsula, which have moderate seismic activity but no available records of strong ground motion, synthetic seismograms can be used to evaluate ground motion without waiting for a strong earthquake. Such seismograms represent the estimated ground motions expected from a set of possible earthquake scenarios. Local site effects are especially important in assessing the seismic hazard and possible ground motion scenarios for a specific fault. The earthquake source and rupture dynamics can be described as a two-step process of rupture initiation and front propagation controlled by a frictional sliding mechanism. The seismic wavefield propagates through heterogeneous geological media and finally undergoes near-surface modulations such as amplification or deamplification. This is a complex system in which various scales of physical phenomena are integrated. A unified approach incorporates multi-scale problems of dynamic rupture, radiated wave propagation, and site effects into an all-in-one model using a three-dimensional, fourth-order, staggered-grid, finite-difference method. The method explains strong ground motions as products of complex systems that can be modified according to a variety of fine-scale rupture scenarios and friction models. A series of such deterministic earthquake scenarios can shed light on the kind of damage that would result and where it would be located.

Robust Parameter Design via Taguchi's Approach and Neural Network

  • Tsai, Jeh-Hsin;Lu, Iuan-Yuan
    • International Journal of Quality Innovation
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    • 제6권1호
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    • pp.109-118
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    • 2005
  • The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

The New Criteria of Dissolved Gas Analysis for Oil-Filled Transformers Using a Cumulative Distribution Function

  • Cho, Sung-Min;Kim, Jae-Chul;Kweon, Dong-Jin;Koo, Kyo-Sun
    • 조명전기설비학회논문지
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    • 제21권9호
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    • pp.87-94
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    • 2007
  • This paper presents new criteria for DGA(Dissolved Gases Analysis) using CDF(Cumulative Distribution Function) obtained from the data from the diagnosis of transformers operated in KEPCO over a period of 16 years. Because of differences in operating environments, construction type, oil volume, and other factors, the interpretative criteria of DGA at KEPCO differs from other standards such as IEC-60599, or Rogers and Doernenburg. To suggest the most appropriate criteria, the DGA data from transformers under normal conditions as well as from developing fault transformers were collected. Using these data, this study suggests the limitative gas level of transformers under normal operating conditions and verifies the suitability of the criteria. Because the application of this new criterion to transformers at KEPCO increases the detectable ratio of incipient faults and reduces unnecessary follow-up sampling and analysis, the new criteria yields a more reliable prediction of transformer condition.

운항 중 실구조물(항공기 축소모델)에서의 탄소섬유강화플라스틱(CFRP)의 음향방출신호 특성에 관한 연구 (A Study on Acoustic Emission Characteristics of CFRP in aircraft operations)

  • 이경원;안주선;황웅기;이종오;이상율;이보영
    • 한국항공운항학회지
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    • 제18권4호
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    • pp.59-66
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    • 2010
  • Aerospace structures need high stability and long life because many personal injuries can result from an accident and securing structural integrity for various external environments is more important than any other thing. So first of all we must prove the destruction properties for operating environment, have prediction technology about damage evolution and life, and develop an economical non-destructive technology capable of detecting structure damage. Acoustic emission (AE) have no need of artificial environment like ultrasonic inspection or radio fluoroscopy to emit a certain energy, is a testing technique using seismic signal resulting from interior changes of solids, and enables to observe if any fault is appeared and it grows seriously or not while running. In this study we suggest the method of structural integrity evaluation for aerospace structures through the acoustic emission technique, for which a model plane was manufactured and an actual operation test was conducted.