Research of time-varying characteristics for component cooling pump failure rate

GUO Haikuan, ZHAO Xinwen, CAI Qi, ZHANG Yongfa, LI Dongxing

Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (3) : 799-804.

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PDF(731 KB)
Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (3) : 799-804. DOI: 10.12011/1000-6788(2017)03-0799-06

Research of time-varying characteristics for component cooling pump failure rate

  • GUO Haikuan1, ZHAO Xinwen1, CAI Qi1, ZHANG Yongfa1, LI Dongxing2
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Abstract

Component cooling pump has the characteristics of high reliability and long lifetime, its failure data have obvious small sample problem. The component cooling pump failure are caused by lossy factors of impact, shaking, abrading, corrosion, and the failure rate λ has time trend, the λ will raise (decrease) with the increase of time. The Jeffreys prior model of invariable λ couldn't replicate failure data for component cooling pump very well, so it couldn't analyse the time-varying characteristics of λ. This paper introduced additional time-varying characteristics for λ on the base of logarithmic linear model (LLM), built generalized linear model (GLM) for Poisson distribution and assessed the time trend of λ and inspected the model through posterior predictive distribution, assessed the model ability of replicating observed data by graph inspection and Bayesian chi-square. The results shown GLM had well fit index and was more propitious to assess the failure rate λ of component cooling pump.

Key words

component cooling pump / Jeffreys prior / Poisson distribution / generalized linear model

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GUO Haikuan , ZHAO Xinwen , CAI Qi , ZHANG Yongfa , LI Dongxing. Research of time-varying characteristics for component cooling pump failure rate. Systems Engineering - Theory & Practice, 2017, 37(3): 799-804 https://doi.org/10.12011/1000-6788(2017)03-0799-06

References

[1] 韦来生,张伟平.贝叶斯分析[M].合肥:中国科学技术大学出版社, 2013: 47-55.
[2] Atwood C L. Constrained noninformative priors in risk assessment[J]. Reliability Engineering and System Safety, 1996, 53(1): 37-46.
[3] 何#
[100],张彬彬.应用Jeffreys方法计算可靠性参数和始发事件频率的无信息先验[J].原子能科学技术, 2013, 47(11): 2059-2062.He J, Zhang B B. Calculation of noninformative prior of reliability parameter and initiating event frequency with Jeffreys method[J]. Atomic Energy Science and Technology, 2013, 47(11): 2059-2062.
[4] 沈志远, 陈伟,袁建新,等.基于Jeffreys先验的PSA通用数据贝叶斯处理方法[J].核动力工程, 2014, 35(6): 84-87.Shen Z Y, Chen W, Yuan J X, et al. Bayesian method of PSA generic data processing based on Jeffreys prior[J]. Nuclear Power Engineering, 2014, 35(6): 84-87.
[5] 谭芙蓉,江志斌,白同朔.大型发电机组早期故障率的统计分析[J].上海交通大学学报, 2005, 39(12): 2093-2096.Tan F R, Jiang Z B, Bai T S. A statistic analysis of early failure rates of large-size generating units[J]. Journal of Shanghai Jiaotong University, 2005, 39(12): 2093-2096.
[6] 茆诗松,汤银才.贝叶斯统计[M]. 2版.北京:中国统计出版社, 2012: 88-97.
[7] Rodionov A, Kelly D, Uwe-Klugel J. Guideines for analysis of data related to ageing of nuclear power plant components and systems[D]. Joint Research Center, Institute for Energy, Luxembourg: European Commission, 2009.

Funding

Nuclear Reactor System Design Technology Foundation of National Key Laboratory (HT-JXYY-02-2014002)
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