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결정론적 화재방호요건을 기반으로 한 원자력발전소 화재모델링 현장실사 수행절차 개발
문종설,이재호 한국화재소방학회 2019 한국화재소방학회논문지 Vol.33 No.6
본 연구에서는 결정론적 화재방호 요건을 기반으로 하는 원자력발전소 화재모델링 현장실사(Walk-down) 수행절차를 개발하였다. 원전 화재모델링 현장실사 수행절차는 도면에 정확히 표시되지 않은 안전정지 기기 및 케이블에대한 위치를 파악하고, 가연물 및 점화원의 존재여부와 위치를 파악하는 과정을 포함한다. 본 연구에서 개발한 현장실사 절차의 성능을 검증하기 위해서 가상의 다중오동작 시나리오에 대해 샘플 안전정지 중요기기 및 케이블을 선정하였다. 또한 선정된 안전정지 기기 및 케이블을 대상으로 가상의 화재모델링 시나리오를 도출하여 이에 대한 실제 현장실사를 수행하였다. 현장실사를 통해서 수집한 발전소 정보는 화재모델링을 위한 입력 값을 도출하는데 사용될 수 있도록 도면에서 얻은 정보와 비교·검토 되었다. A walk-down procedure for fire modeling of nuclear power plants, based on deterministic fire protection requirements,was developed. The walk-down procedure includes checking the locations of safety shutdown equipment and cables that arenot correctly indicated on drawings and identifying the existence and location of combustibles and ignition sources. In orderto verify the performance of the walk-down procedure developed in this study, a sample of important equipment and cableswere selected for hypothetical multiple spurious operation (MSO) scenarios. In addition, the hypothetical fire modelingscenarios were derived from the selected safe shutdown equipment and cables and an actual walk-down was conducted. Theplant information collected through the walk-down was compared to the information obtained from the drawings, so thatthe collected information may be used as input values for the fire modeling.
홍영선,윤해성,문종설,조영만,안성훈 한국정밀공학회 2016 International Journal of Precision Engineering and Vol.17 No.7
Tool wear is one of the most important parameters in micro-end milling, and can be used to monitor the condition of the machine and the tool. A micro-end mill has different characteristics from a macro-scale end mill; in particular, shank run-out (which is negligible in the macro-scale tool due to the low aspect ratio) is significant in micro-end milling, inducing excessive tool wear and reduced tool life and leading to sudden, premature failure. In this paper, a novel tool-wear monitoring method is described for determining the state of a micro-end mill using wavelet packet transforms and Fisher’s linear discriminant. Force and torque signals were measured using a dynamometer and were used to reflect geometric changes in the micro-end mill due to wear. Because of the small signal-to-noise ratio, sensor signals measured during the milling process were periodically averaged, and the resulting singleperiod signals provided improved efficiency of feature extraction using wavelet packet transforms. The extracted features were classified in the wavelet domain and used to determine the tool state employing a hidden Markov model. The recognition results were compared with those of an energy-based monitoring technique, and we found that our method could determine the tool state more accurately for both normal wear and premature failure of micro-end mills.