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문지섭(Ji Seob Moon),정선용(Seon Yong Jeong),김명수(Myung Soo Kim) 한국신뢰성학회 2011 신뢰성응용연구 Vol.11 No.3
This paper presents an accelerated life test of booster pump for home water purifier. The failure analysis shows that decreased flux due to the plastic deformation of bypass spring adjusting pressure is the predominant failure mechanism. An accelerated life test is designed and implemented to estimate the lifetime of the booster pump. Temperature, water pressure and voltage are selected as accelerating variables through the technical review about failure mechanism. It is assumed that the lifetimes of booster pumps follow lognormal distribution and the combination model of temperature and non-thermal stresses holds. The life-stress relationship, acceleration factor, and B10 life at design condition are estimated by analyzing the accelerated life test data.
문지섭(Ji Seob Moon),이성민(Sung Min Lee),정선용(Seon Yong Jeong),김명수(Myung Soo Kim) 한국신뢰성학회 2014 신뢰성응용연구 Vol.14 No.1
This paper presents an accelerated life test to estimate the lifetime of thermoelectric module for home water purifier. Clamping force and thermal cycle are selected as accelerating variables through the technical review about failure mechanism. It is assumed that its lifetime follows weibull distribution. The relationship, acceleration factor, and BP life at design condition are estimated by analyzing the accelerated life test data.
도덕희,김동혁,방광현,문지섭,홍성대,장태현,황태규,Doh Deog Hee,Kim Dong Hyuk,Bang Kwang Hyun,Moon Ji Seob,Hong Seong Dae,Chang Tae Hyun,Hwang Tae Gyu 한국마린엔지니어링학회 2005 한국마린엔지니어링학회지 Vol.29 No.2
Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. 1K $\times$ 1K CCD color camera and Xenon Lamp(500w) were used for the visualization of a Hele-Shaw cell The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.