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Optimal Scheduling of Tests of Safety Systems, Considering Test-Induced Degradation
... In particular, the application of the Nelder-Mead method, commonly used in nonlinear optimization problems as addressed in Lin and Pham (2022), did not produce better results compared to AG for this application. In addition, analytical methods that require the probability of failure to be constant over time cannot be applied to the problems in which nonhomogeneous counting processes are necessary, such as the examples dealt with in this work (Hafver et al., 2019). In Figure 2, the admitted intervention levels depend on the application and must be provided by the user. ...
... For instance, the utilization of the Nelder-Mead method, which is commonly employed in solving nonlinear optimization problems, as discussed in Lin and Pham's study [17], did not yield superior outcomes compared to metaheuristic methods in this specific application. Moreover, analytical techniques that assume a constant probability of failure over time cannot be utilized for problems that involve non-homogeneous counting processes, similar to the examples examined in this research [18]. ...
Maintenance policies are crucial for ensuring the reliability, safety, and longevity of a system , as well as reducing the risk of accidents. Preventive maintenance (PM) is an effective strategy to keep equipment and systems in good working order by fixing potential issues before they cause downtime or safety hazards. However, optimizing the time intervals between PM activities is essential for minimizing the overall maintenance cost. This paper proposes an innovative approach that considers the intervention level of maintenance activities as an independent variable of PM times. The approach provides greater flexibility in creating maintenance plans, as it considers practical aspects that may impact maintenance activities beyond the time interval between PMs. The proposed approach uses a reliability model that incorporates imperfect preventive maintenance and a variable improvement factor based on age reduction. The improvement factor of each preventive maintenance activity (PMA) is defined based on the intervention level of the activity itself, which is determined by the number of tasks performed, execution time, and the number of items replaced in the maintenance plan. The proposed maintenance strategy determines not only the optimal times for PMAs and the respective intervention level but also the optimal number of maintenance activities that minimize the total maintenance cost along a fixed and user-defined planning horizon. The effectiveness and precision of the approach have been demonstrated through a series of numerical examples and a comprehensive case study involving three heat exchangers situated within the hydroelectric power plant.
Redundancy is a widely adopted measure for the enhancement of safety system performance. This fault-tolerance design can keep the system functioning through one unit if the other has failed. Besides the continuous aging degradation, mechanical units expose to random shocks, resulting in the dependence. In practice, it is often sufficient to activate only one unit in case of a shock so that the degradation will then be intensified. Unit-level failure occurs when the total degradation level reaches a certain level and will keep hidden. Thus, periodic tests are arranged to reveal the system state, accompanying the side effects on the existing degradation given the test-induced additional stress. A novel condition-based activation policy is proposed based on the system state, referring to how to preset the allocation of units to withstand potential shocks in the upcoming test interval. In this paper, analytical formulas are developed to evaluate system performance with the involvement of these aforementioned factors. Finally, numerical examples are presented to find an optimal test and activation policy to reach minimum system unavailability in a given service time horizon. This study is expected to provide clues for practitioners in the optimal test and operation of redundant structure.
Determining appropriate maintenance programmes for technical inventory is recognized as important for quality reliability and safety management in the oil and gas industry. The programme could be achieved through reliability-centred maintenance (RCM) analysis, where safety-critical equipment with potential for hidden failures is given particular attention. Output of the analysis is seen in combination with relevant requirements to perform functional testing of the equipment. The testing involves collecting and analysing data for verification of acceptable reliability and safety levels during the operational phase. This testing is often required in periodic intervals, where shorter intervals might be required initially or after failures for more control. Despite the intention of such activity, it could however influence equipment conditions in a negative way and over time contribute to a reduced reliability performance, i.e., lead to maintenance-induced failures. In this paper, focus is on periodic testing of the component ‘downhole safety valve’ (DHSV), and mechanisms leading to its failure. We consider the use of an age-adjusting imperfect repair model for analysing the effect of maintenance-induced DHSV failures and discuss the influence of recommended industry guidance. We particularly discuss the benefits of a test strategy having initially one to three months intervals, compared with an alternative strategy with constant six-month or one-year intervals. Based on the analysis, the 12-month interval gives the highest overall probability of failure on demand despite reducing the probability for maintenance-induced failures. There is a marginal difference between the other two alternatives, where then the selected distributions and uncertainties play a larger role. Barrier data collected by the Petroleum Safety Authority Norway (RNNP project data) is used for the analysis.
In this paper, we discuss the inspection policy for the modified inspection model taking account of the system failure due to any inspection. We obtain the nearly optimal inspection policy which minimizes the nearly total expected cost up to the detection of the system failure. We also present the numerical examples of the nearly optimal inspection policy by assuming a Weibull distribution for illustration.
Technical specifications for nuclear power plants require periodic surveillance testing of the standby systems important to safety. This regulatory requirement is imposed to assure that the systems will start and perform their intended functions in the event of plant abnormality. However, operating experience suggests that, in addition to the beneficial effects of detecting latent faults, the tests may have adverse effects on the plant's operation or equipment. This paper defines those adverse effects of testing from a risk perspective, and then presents a method to quantify their associated risk impact, focusing on plant transients and the wear-out of safety systems. The method, based on probabilistic safety assessment, is demonstrated by applying it to several surveillance tests conducted at boiling water reactors. The insights from this evaluation can be used to determine risk-effective intervals for surveillance tests.
Nuclear power plant systems are comprised of both on-line and standby components. Standby components differ from on-line ones, as they might be unavailable due to unrevealed failures. The usual procedure employed to reveal failures before real demands is to submit the component to surveillance tests. Surveillance test policies might deal with two conflicting scenarios: the test frequency must be sufficiently high in order to reveal failures before demands, but, on the other hand, it must be low enough due to its influence on the component unavailability.
Standard surveillance test policies for typical nuclear power plants usually consist of periodic tests for which the frequencies are often higher than necessary for obtaining the optimal availability. In this work, a new surveillance test policy optimization method, based on genetic algorithms, is applied to the Angra-I (Brazilian PWR) auxiliary feedwater system. The new probabilistic model has been developed in order to comprise the following features: (1) aging effects on standby components when they undergo surveillance tests; (2) revealing failures during the surveillance tests implies corrective maintenance, and, consequently, increasing outage times; (3) components are distinct (i.e., each has distinct test parameters, such as outage time, aging factors, etc); (4) tests are not necessarily periodic. The results, when compared to those obtained by standard test policies, show improved overall availability at the system level.
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