• Title/Summary/Keyword: (열전파 알고리즘)

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Development of A Fault Diagnosis System for Assembled Small Motors Using ANN (인공신경회로망을 이용한 소형 모터의 조립 불량 판별 시스템 개발)

  • Lee, Sang-Min;Jo, Jung-Seon
    • Journal of the Korean Society for Precision Engineering
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    • v.18 no.11
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    • pp.124-131
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    • 2001
  • Fault diagnosis of an assembled small motor relies usually on human experts hearing ability. The quality of diagnosis depends, however, heavily on physical conditions of the human experts. A fault diagnosis system for assembled small motors is developed using artificial neural network (ANN) in this paper. It is consisted of sound sampling device and fault diagnosis software package. Six parameters are defined to characterize the sampled sound waves. The Levenberg-Marquardt Backpropagation (LMBP) Algorithm is used to diagnose the fault of assembled small motors. Experimental results for more than two hundred small motors verify the performance of the developed system.

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A Study on the Generation and Transmission of a Pressure Wave Induced by Rapid Heating of Compressible Fluid (압축성 유체의 급속 가열에 기인한 압력파의 생성 및 전달특성에 관한 연구)

  • 황인주;김윤제
    • Journal of Energy Engineering
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    • v.12 no.1
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    • pp.29-34
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    • 2003
  • Thermo-acoustic waves can be generated in a compressible fluid by rapid heating and cooling near the boundary walls. These phenomena are very important mechanism of heat transfer in the space environment in which natural convection does not exist. In this study, the generation and transmission characteristics of thermo-acoustic waves in an air filled enclosure with rapid wall heating are studied numerically. The governing equations were discretized using control volume method, and were solved using PISO algorithm and second-order upwind scheme. For the stable solution time step were considered as t=1$\times$$10^{-9}$ order, and grids are 50$\times$800. The induced thermo-acoustic wave propagates through the fluid until it decays due to viscous and heat dissipation. The wave showed sharp front shape and decreased with long tail.

A Heatmap-based Leakage Location Estimation Algorithm for Circulating Fluidized Bed Boiler Tube Using Acoustic Emission Sensors (음향방출 센서를 이용한 히트맵기반 순환유동층 보일러 튜브 누설 위치 추정 알고리즘)

  • Kim, Jaeyoung;Kim, Jong-Myon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2018.07a
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    • pp.51-52
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    • 2018
  • 화력발전용 순환유동층 보일러는 환경오염의 주요인인 질소산화물(NOx)과 황산화물(SOx)의 배출량이 적은 친환경 화력발전용 보일러로 화력발전 업계에서 각광받고 있는 추세이다. 그러나 순환유동층 보일러의 연료인 유동매체는 미분탄과 같이 작지만 단단한 고체이므로 유동매체의 타격으로 인해 워터월(waterwall) 튜브의 마모는 물론 누설까지 야기할 수 있다. 순환유동층 보일러 튜브에서 누설된 증기는 보일러 내부에 클링커(Clinker)를 발생시키고 이는 순환유동층 보일러 튜브 표면에 응고되어 열전도율을 감소시킬 뿐만 아니라 보일러 운전정지의 원인이 된다. 따라서 본 논문에서는 음향방출 센서를 이용하여 화력발전용 순환유동층 보일러 튜브의 누설 위치를 추정하는 방법을 제안한다. 제안 방법에서는 매질의 분자단위 이동에 의해 발생되는 탄성파를 감지할 수 있는 음향방출 센서를 이용하고, 보일러 워터월 튜브의 멤브레인 용접부와 비용접부(seamless)의 감쇠율을 고려한 위치별 센서 감도 추정 알고리즘을 통해 워터월 튜브의 위치별 진폭 크기를 히트맵으로 표현할 수 있다.

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Temperature Prediction of Underground Working Place Using Artificial Neural Networks (인공신경망을 이용한 심부 갱내온도 예측)

  • Kim, Yun-Kwang;Kim, Jin
    • Tunnel and Underground Space
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    • v.17 no.4
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    • pp.301-310
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    • 2007
  • The prediction of temperature in the workings for the propriety examination for the development of a deep coal bed and the ventilation design is fairly important. It is quite demanding to obtain precise thermal conductivity of rock due to the variety and the complexity of the rock types contiguous to the coal bed. Therefore, to estimate the thermal conductivity corresponding to this geological situation and complex gallery conditions, a computing program which is TemPredict, is developed in this study. It employs Artificial Neural Network and calculates the climatic conditions in galleries. This advanced neural network is based upon the Back-Propagation Algorithm and composed of the input layers that are acceptant of the physical and geological factors of the coal bed and the hidden layers each of which has the 5 and 3 neurons. To verify TemPredict, the calculated result is compared with the measured one at the entrance of -300 ML 9X of Jang-sung production department, Jang-sung Coal Mine. The difference between the results calculated by TemPredict ($25.65^{\circ}C$) and measured ($25.7^{\circ}C$) is only $0.05^{\circ}C$, which is less than the allowable error 5%. The result has more than 95% of very high reliability. The temperature prediction for the main carriage gallery 9X in -425 ML under construction when it is completed is made. Its result is $28.2^{\circ}C$. In the future, it would contribute to the ventilation design for the mine and the underground structures.