Data Reliability Analysis during Containment Leakage Test Based on Statistical Software R

被引:0
|
作者
Shen D. [1 ]
Cai J. [1 ]
He R. [1 ]
Huang X. [2 ]
机构
[1] State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen, 518172, Guangdong
[2] School of energy and power engineering, Huazhong University of Science and Technology, Wuhan
来源
关键词
Containment; Leakage test; Regression diagnosis; Significance test;
D O I
10.13832/j.jnpe.2020.05.0099
中图分类号
学科分类号
摘要
The most important part in the calculation of the containment leakage is to perform the linear regression on time for a series of data measured at different times. The significance test of the regression and residual analysis are the substantial means to evaluate the test results. This paper analyzes the data of the containment test during the commissioning and startup phase of a power plant based on the statistical software R, and explores the regression diagnosis before the leakage calculation by examining the independence, normality and heteroscedasticity of the regression model and the elimination of extreme sample points impact on the reliability of the result. Through the regression diagnosis on the examples, it was found that in the samples which leakage rate is calculated, there may be problems that affect the regression results and then the affect the final results, such as autocorrelation, non-normality and heteroscedasticity. Therefore, the validity of the data shall be evaluated by the regression diagnostic methods, while calculating the leakage rate, and the final results shall be corrected by appropriate methods for samples that fail the test. © 2020, Editorial Board of Journal of Nuclear Power Engineering. All right reserved.
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页码:99 / 103
页数:4
相关论文
共 3 条
  • [1] Containment system leakage testing requirements: ANSI/ANS-56.8-2002, pp. 14-15, (2002)
  • [2] GARY O, ZERBE, On fieller's theorem and the general liner model, The American Statistician, 32, pp. 103-105, (1979)
  • [3] CHRISTIAN K, ACHIM Z., Applied econometrics with R, pp. 94-106, (2008)