Planning Surveillance Test Policies Through Genetic Algorithms
Nuclear power plant systems are comprised of both online and cold-standby components. Cold-standby components differ from the online ones, as they may be unavailable due to unrevealed failures. The usual procedure employed to reveal failures is to submit the components to surveillance tests. The surveillance tests policy may deal with two conflicting scenarios: the test frequency must be sufficiently high in order to minimize the occurrence of failures but, on the other hand, it must be low enough due to its influence on the component unavailability. Obtaining an optimum surveillance test policy at the system level, which involves consideration of many interdependent components, is a very difficult optimization problem. Hence, in this work we propose the use of genetic algorithms for searching the optimum surveillance tests policy, due to its robustness and efficiency in complex problem solving. Here, the probabilistic model considers: a) wear-out effects on cold-standby components when they undergo surveillance tests; b) that when a failure is revealed during a surveillance test, corrective maintenance is performed and, c) that components are distinct (that is, each has distinct test parameters, such as: outage time, wear-out factors, etc.); d) that tests are not necessarily periodic.
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