NAROAS: a neural network-based advanced operator support system for the assessment of systems reliability

• Published in: Reliability engineering & system safety 2005-02, Vol.87 (2), p.149-161
• Main Authors: Gromann de Araujo Góes, A., Alvarenga, M.A.B., Frutuoso e Melo, P.F.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer systems performance. Reliability; Connectionism. Neural networks; Decision support system; Exact sciences and technology; Hopfield neural network; Risk prioritization; Software; System configuration control
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2004.01.010

We have developed and implemented a computerized reliability monitoring system for nuclear power plant applications, based on a neural network. The developed computer program is a new tool related to operator decision support systems, in case of component failures, for the determination of test and maintenance policies during normal operation or to follow an incident sequence in a nuclear power plant. The NAROAS (Neural Network Advanced Reliability Advisory System) computer system has been developed as a modularized integrated system in a C++ Builder environment, using a Hopfield neural network instead of fault trees, to follow and control the different system configurations, for interventions as quickly as possible at the plant. The observed results are comparable and similar to those of other computer system results. As shown, the application of this neural network contributes to the state of the art of risk monitoring systems by turning it easier to perform online reliability calculations in the context of probabilistic safety assessments of nuclear power plants.