The Optimal Combination Research of System Configuration and T&M Policy of Standby Safety System

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Abstract:

The unavailability equations of several aging scenarios of standby safety component are derived and the risk of standby safety system is quantified. The different maintenance strategies are adopted for no aging scenario and several aging scenarios respectively. The two kind of test and maintenance (T&M) policies are adopted for no aging scenario and several aging scenarios. A numerical example is introduced for the illustration. The system unavailabilities under different configurations of parallel safety system and T&M policies are computed and compared. It can be derived that the combination of different T&M policies and configurations is significant effect on the risk of standby safety system and optimal STI and T&M interval.

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6-11

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June 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Duško Kančev, Marko Čepin. Evaluation of risk and cost using an age-dependent unavailability modelling of test and maintenance for standby components [J]. Journal of Loss Prevention in Process Industries, 24(2), 2011:146-155.

DOI: 10.1016/j.jlp.2010.12.003

Google Scholar

[2] Ebrahimipour, Suzuki, Azadeh, 2006. A synergetic approach for assessing and improving equipment performance in offshore industry based on dependability [J]. Reliability engineering and system safety, 91(1), 2006:10-19.

DOI: 10.1016/j.ress.2004.11.008

Google Scholar

[3] S.A. Martorell, V.G. Serradell, P.K. Samanta. Improving allowed outage time and surveillance test interval requirements: a study of their interactions using probabilistic methods [J]. Reliability engineering and system safety, 47(1), 1995:119-129.

DOI: 10.1016/0951-8320(94)00043-n

Google Scholar

[4] R.J. Cizelj, B.Mavko, I.Kljenak. Component reliability assessment using quantitative and qualitative data [J]. Reliability engineering and system safety, 2001, 71:81-95.

DOI: 10.1016/s0951-8320(00)00073-9

Google Scholar

[5] Marko Čepin, Borut Mavko. A dynamic fault tree [J].Reliability engineering and system safety, 75, 2002; 83-91.

DOI: 10.1016/s0951-8320(01)00121-1

Google Scholar

[6] Radim Briš. Parallel simulation algorithm for maintenance optimization based on directed Acyclic Graph [J]. Reliability engineering and system safety, 93, 2008:852-862.

DOI: 10.1016/j.ress.2007.03.036

Google Scholar

[7] M.Marseguerra, E.Zio. Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation [J]. Reliability engineering and system safety, 68, 2000:69-83.

DOI: 10.1016/s0951-8320(00)00007-7

Google Scholar

[8] Duško Kančev, Blaže Gjorgiev, Marko Čepin. Optimization of test interval for aging equipment: A multi-objective genetic algorithm approach [J]. Journal of Loss Prevention in Process Industries, 24(4), 2011:397-404.

DOI: 10.1016/j.jlp.2011.02.003

Google Scholar

[9] M.Marseguerra, E.Zio. Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation [J]. Reliability engineering and system safety, 68, 2000:69-83.

DOI: 10.1016/s0951-8320(00)00007-7

Google Scholar

[10] J.K. Vaurio.Optimization of test and maintenance intervals based on risk and cost [J]. Reliability engineering and system safety, 49(1), 1995:23-36.

DOI: 10.1016/0951-8320(95)00035-z

Google Scholar

[11] M. Čepin, B. Mavko. Probabilistic safety assessment improves surveillance requirements in technical specifications [J]. Reliability engineering and system safety, 56(1), 1997:69-77.

DOI: 10.1016/s0951-8320(96)00138-x

Google Scholar

[12] J.K. Vaurio. Safety-related decision making at a nuclear power plant [J]. Nuclear engineering and design, 185(2-3), 1998:335-345.

DOI: 10.1016/s0029-5493(98)00225-8

Google Scholar