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Ieee NPSS conference on Real Time

作者： chang Jae Lee Seung Han Jae Whal Shin Jae Hee Yun Instrumentation and Control System Engineering Group of KEPCO E&C Daejeon Korea

ISBN: (纸本)9781509020157

Safety analysis for a nuclear power plant establishes not only an analytical limit in terms of a measured or calculated variable but also an analytical response time required to complete protective action after the analytical limit is reached. If the two constraints are met, the safety limit selected to maintain the integrity of physical barriers used for preventing uncontrolled radioactivity release will not be exceeded during anticipated operational occurrences and postulated accidents. Setpoint determination methodologies have been actively developed to ensure that protective action is initiated before the process conditions reach the analytical limit. However, regarding the analytical response time for the plant protection system (PPS), an integrated evaluation methodology considering the whole design process has not been systematically studied. In order to assure the safety of nuclear power plants, this paper proposes a systematic and integrated response time evaluation methodology that covers safety analyses, system designs, response time analyses, and response time tests. This methodology is applied to the PPS for the advanced power reactor 1400 nuclear power plant in Korea. The quantitative evaluation results are provided herein. The evaluation results using the proposed methodology demonstrate that the PPS completely satisfies the requirement of the analytical response time.

关键词： Decision support systems Time factors Safety

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AccURAcY ReQUIReMeNTS FOR MeASUReMeNT systemS WITH UNIFORM AND NORMAL eRRORS

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NAVAL eNGINeeRS JOURNAL 1993年第1期105卷 66-69页

作者： SNOW, JM STUcKMAN, Be USHeR, JS Jeffrey M. Snow:received his B.S. degree in mechanical engineering from the University of Washington in 1977. He has worked at Boeing Commercial Aircraft Company and Puget Sound Naval Shipyard. Since 1985 he has been at Naval Surface Warfare Center Crane Division (NSWCC). From 1987 to 1990 he was in charge of quality assurance configuration management and reliability for Aegis (SPY-1 and Mk-99) microwave tubes. He currently is in the Radar System Branch at NSWCC. He is a member of ASQC and ASME. His areas of interest include the use of statistics in process improvement efforts and objectively rating and rewarding suppliers for better quality. Bruce E. Stuckman:holds a Ph.D. degree in systems engineering from Oakland University where he also obtained M.S. and B.S. degrees in electrical and computer engineering. In 1992 Dr. Stuckman obtained his J.D. from the University of Louisville where he graduated cum laude. He is currently an associate in the patent and computer law department of Brooks & Kushman a law firm specializing in intellectual property and technology related causes. Prior to this he was an associate professor of both electrical engineering at the University of Louisville and a member of the Research Advisory Board of the South Central Bell Telecommunications Research Center. Dr. Stuckman is the author of over 75 published works in a variety of research areas including global optimization algorithms control systems communication and telecommunication systems and instrumentation electronics. He has served as a technical consultant to such organizations as Control Data Corporation General Electric General Motors Research Laboratories Gulf & Western AT&T Bell Laboratories ARCO Research and Technical Services and the U.S. Government and has been a frequent lecturer at national and international symposia. He is a senior member of IEEE and a member of ASNE Eta Kappa Nu Tau Beta Pi Sigma Xi and Phi Kappa Phi. John S. Usher Ph.D. P.E.:is an assistant professor of industrial engineering

Accuracy requirements are usually determined as a percentage of the specification range of the measured part or process. Setting accuracy requirements in this manner results in a wide and unpredictable range of false rejection and acceptance probabilities. This causes extra costs due to either: 1) over specification of measurement systems accuracy requirements;2) time, effort, retesting, and resolution of false rejections;or 3) system degradation caused by false acceptance of out-of-specification parts. Achieving a consistent and known risk of false acceptance is only possible by considering the measured process c(pk), the process's mean in relation to the center of the specification range, and the measurement system error distribution. This paper presents a method for calculating the probabilities of false rejection and false acceptance for a normal process which is measured with, alternately, uniform and normally distributed error. It is shown that under most conditions uniform error causes 20% to 30% higher false rejection and acceptance probabilities. Thus, knowledge of measurement error distribution could provide lower total production cost.

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