Optimisation of ISI interval using genetic algorithms for risk informed in-service inspection

摘要

Risk Informed In-Service Inspection (RI-ISI) aims at prioritising the components for inspection within the permissible risk level therebyavoiding unnecessary inspections. Various constraints such as risk level, radiation exposure to the workers and cost of inspections areencountered, while planning the inspection programme. This problem has been attempted to solve using genetic algorithms, which hasalready established its suitability in optimizing Surveillance and Maintenance activities in Nuclear Power Plants. The paper describes theapplication of genetic algorithm in optimizing the ISI of feeders, which are large in number and also fall in the same inspection category.