• 제목/요약/키워드: Maintenance Periods Optimization

검색결과 12건 처리시간 0.025초

발전기 예방정비계획 전산모형 개발 (Development of Generator Maintenance Scheduling Program)

  • 박종배;정윤원;주행로;이명희;신점구
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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    • pp.216-217
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    • 2006
  • This paper presents development of program for generator maintenance scheduling. The maintenance scheduling of generating units is a dynamic discrete combinatorial optimization problem with constraints to determine the optimal maintenance periods of each generating units for a given planning periods. The developed program is designed so as to provide the maintenance schedule satisfying the operating reserve margin levelization and the procurement of proper reliability. In order to verify the effectiveness of the developed program, the numerical study has been performed with the practical data in 2005.

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Control system modeling of stock management for civil infrastructure

  • Abe, Masato
    • Smart Structures and Systems
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    • 제15권3호
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    • pp.609-625
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    • 2015
  • Management of infrastructure stock is essential in sustainability of society, and its analysis and optimization are studied in the light of control system modeling in this paper. At the first part of the paper, cost of stock management is analyzed based on macroscopic statistics on infrastructure stock and economical growth. Stock management burden relative to economy is observed to become larger at low economic growth periods in developed economies. Then, control system modeling of stock management is introduced and by augmenting maintenance actions as control input, dynamic behavior of stock is simulated and compared with existing time history statistics. Assuming steady state conditions, applicability of the model to cross sectional data is also demonstrated. The proposed model is enhanced so that both preventive and corrective maintenance can be included as system inputs, i.e., feedforward and feedback control inputs. Optimal management strategy to achieve specified deteriorated stock level with minimal cost, expressed in terms of preventive and corrective maintenance actions, is derived based on estimated parameter values for corrosion of steel bridges. Relative cost effectiveness of preventive maintenance is shown when target deteriorated stock level is lower.

상태기반정비에 의한 증기터빈 저널베어링의 정비주기 최적화 (Maintenance Frequency Optimization of the Steam Turbine Journal Bearings by Condition-based Maintenance)

  • 이혁순;정혁진;송우석
    • 한국압력기기공학회 논문집
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    • 제7권2호
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    • pp.7-13
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    • 2011
  • Turbine journal bearings are designed to support the weight of the rotors on a hydrodynamic oil film and to provide dynamic stability to the rotor system. The life time of journal bearings is infinite theoretically because the journal bearings are separated from the shaft journal by oil film. But poor design, assembly, operation and maintenance can cause problems to the journal bearings. The FMEA(Failure Mode and Effects Analysis) results of the journal bearings show that frequent maintenance of the journal bearings can cause failures and reduction of the bearing life. Therefore, the maintenance periods and history of the journal bearings with the bearing FMEA results are reviewed in order to establish the optimized maintenance period of the journal bearing for the nuclear power plants. Consequently it is necessary to maintain a best condition of lubrication system, reject time-based maintenance and perform the condition-based maintenance of journal bearings in order to maintain optimum condition of the journal bearing.

최적경로탐색문제를 위한 인공신경회로망 (An Artificial Neural Network for the Optimal Path Planning)

  • 김욱;박영문
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1991년도 하계학술대회 논문집
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    • pp.333-336
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    • 1991
  • In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.

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해상 연약지반의 저치환율 개량에 대한 확률론적 최적화 (Probabilistic Optimization for Improving Soft Marine Ground using a Low Replacement Ratio)

  • 한상현;김홍연;여규권
    • 지질공학
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    • 제26권4호
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    • pp.485-495
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    • 2016
  • 본 연구에서는 방파제 하부지반을 저치환율 재료로 보강 및 개량하기 위한 치환율과 재하중 방치기간을 확률론적 최적화 기법을 이용하여 분석하였다. 해석에 필요한 확률변수의 불확실성을 최소화하기 위하여 사전자료를 활용한 베이지안 갱신결과 최대 39.8% 포인트까지 불확실성이 감소하였고, 특히 사전함수의 표본수가 더 많은 구간의 감소폭이 컸다. 치환율 결정을 위하여 저치환율 단면 중 15~40% 범위에서 일계신뢰도법 및 몬테카를로 시뮬레이션 방법에 의해 해석한 결과 목표파괴확률을 만족하는 치환율은 심층고결처리 및 쇄석다짐말뚝 구간에서 각각 20% 및 25% 이상으로 나타났다. 치환율에 대한 최적화를 위하여 생애주기비용 분석을 실시한 결과 목표파괴확률을 만족하는 범위 내에서 최적 치환율이 산정되었으며, 두 구간에서 각각 20% 및 30%가 가장 경제적인 것으로 결정되었다. 재하중의 방치기간에 대한 확률론적 해석결과 3개월 이상인 경우 모두 목표파괴확률을 만족하는 것으로 나타났다.

선박용 수냉식 디젤엔진의 개발 및 성능평가 (A Design for Water Cooling of a Marine Diesel Engine with Verification of Improvement)

  • 심한섭;전종오
    • 한국기계가공학회지
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    • 제15권6호
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    • pp.58-63
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    • 2016
  • This paper presents a study of heat dissipation away from the fuel combustion of a marine diesel engine. These engines are operated for long periods under high load conditions: so cooling systems are necessary for radiation and control of the high temperature levels. In the study, each component of the water cooling system was developed to achieve improvements in cooling and safety. Heat transfer considerations and arrangement design for the components were important and an intercooler and exhaust manifold incorporated. An optimization of the cooling water's flow path was achieved subject to the need for convenient maintenance. The 750Ps marine diesel engine was used for performance testing of the cooling system. The test results showed adequate cooling performance improvement.

온도 변화에 따른 수돗물 저장 저수조 내 잔류염소에 관한 수학적 모형 시뮬레이션 (Mathematical Model Simulations Assessing the Effects of Temperature on Residual Chlorine Concentrations in Water Storage Tanks)

  • 노유래;박준홍
    • 한국물환경학회지
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    • 제33권2호
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    • pp.187-196
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    • 2017
  • To ensure hygienic safety of drinking water in a water storage tank, the concentrations of residual chlorine should be above a certain regulation level. In this study, we conducted model simulations to investigate the effects of temperature on residual chlorine in water storage tank conditions typically used in Seoul. For this, values of model parameters (decomposition rate constant, sorption coefficient, and evaporation mass transfer coefficient) were experimentally determined from laboratory experiments. The model simulations under continuous flow conditions showed that the residual chlorine concentrations were satisfied the water quality standard level (0.1 mg/L) at all the temperature conditions ($5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, $20^{\circ}C$ and $25^{\circ}C$). Meanwhile, when the tanks had a no flow condition (i.e., no tap-water influent due to a sudden shut-down), the concentrations became lower than the regulatory level after certain periods. The findings from this modeling works simulating Seoul's water storage tanks suggested disappearance rate of residual chlorine could be reduced through the tanks design optimization with maintenance of low water temperature, minimization of air flow and volume, suppression of dispersion and the use of wall materials with low sorption ability.

균열 탐지의 의미론적 분할을 위한 Mean Teacher 학습 구조 최적화 (Mean Teacher Learning Structure Optimization for Semantic Segmentation of Crack Detection)

  • 심승보
    • 한국구조물진단유지관리공학회 논문집
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    • 제27권5호
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    • pp.113-119
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    • 2023
  • 인프라 구조물은 대부분 경제 성장기에 완공되었다. 이러한 인프라 구조물은 최근 들어 공용연수가 점차 증가하고 있어 노후 구조물의 비중이 점차 증가하고 있다. 이러한 노후 구조물은 설계 당시의 기능과 성능이 저하될 수 있고 안전사고로까지 이어질 수 있다. 이를 예방하기 위해서는 정확한 점검과 적절한 보수가 필수적이다. 이를 위해서는 우선 미세한 균열까지 정확히 탐지할 수 있도록 컴퓨터 비전과 딥러닝 기술에 수요가 증가하고 있다. 하지만 딥러닝 알고리즘은 다수의 학습 데이터가 있어야 한다. 특히 영상 내 균열의 위치를 표시한 라벨 영상은 필수적이다. 이러한 라벨 영상을 다수 확보하기 위해서는 많은 노동력과 시간이 필요한 실정이다. 이러한 비용을 절감하고 탐지 정확도를 높이기 위해서 본 연구에서는 mean teacher 방식의 학습 구조를 제안하였다. 이 학습 구조는 900장의 라벨 영상 데이터 세트와 3000장의 비라벨 영상 데이터 세트로 훈련되었다. 학습된 균열 탐지 신경망 모델은 300여장의 실험용 데이터 세트를 통해 평가되었고 탐지 정확도는 89.23%의 mean intersection over union과 89.12%의 F1 score를 기록하였다. 이 설험을 통해 지도학습과 비교하여 탐지 성능이 향상된 것을 확인하였다. 향후에 이러한 방법은 라벨 영상을 확보하는데 필요한 비용을 절감하는데 활용될 것으로 기대한다.