Evaluation of risk and cost using an age-dependent unavailability modelling of test and maintenance for standby components

The improvement of safety in the process industries is related to assessment and reduction of risk in a cost-effective manner. This paper addresses the trade-off between risk and cost related to standby safety systems. An age-dependent unavailability model that integrates the effects of the test and maintenance (T&M) activities as well as component ageing is developed and represents the basis for calculating risk. The repair “same-as-new” process is considered regarding the T&M activities. Costs are expressed as a function of the selected risk measure. The time-averaged function of the selected risk measure is obtained from probabilistic safety assessment, i.e. the fault tree analysis. This function is further extended with inclusion of additional parameters related to T&M activities as well as ageing parameters related to component ageing. In that sense, a new model of system unavailability, incorporating component ageing and T&M costs, is presented. The testing strategy is also addressed. Sequential and staggered testings are compared. The developed approach is applied on a standard safety system in nuclear power plant although the method is applicable to standby safety systems that are tested and maintained in other industries as well. The results show that the risk-informed surveillance requirements differ from existing ones in technical specifications, which are deterministically based. Moreover, the presented approach achieves a significant reduction in system unavailability over a relatively small increase of total T&M costs. ► Development of analytical unavailability model integrating T&M, ageing, test strategy. ► The developed approach is applied on a standard stand-by safety system. ► Risk-informed surveillance requirements differ from existing ones in TS. ► Significant reduction in system unavailability achieved by proposing new STI. ► Ageing data uncertainty has substantial impact on the quantitative analyses.