A multi-stage hybrid fault diagnosis approach for operating conditions of nuclear power plant

Abstract

The security and stability of systems and components in operating nuclear power plants (NPPs) are extremely important factors that determine the plants reliability and service life. An essential system that help reduce the maintenance costs and improve the reliability of NPPs is an efficient fault diagnosis system. In this paper, an optimized fault diagnosis framework is proposed to efficiently identify system-level failures and their severities to guarantee the sustainability of NPPs. To facilitate the recognition of the real-time failure types and to extract both the constraint relationships and fault regularities of system-level parameters, the least squares support vector machine (LS-SVM) method was introduced at the fault diagnosis step. The severity assessment to estimate the degree of failures was subsequently performed using a method derived from gaussian process regression (GPR). To overcome the challenge of selecting hyperparameters of GPR, the particle swarm optimization (PSO) was applied to search for the optimal hyperparameters of GPR. The PSO intelligent search strategy implemented to obtain a fault severity assessment model ultimately aided operators to make an informed decision on the operating conditions of the plant. Simulations carried out based on the proposed fault diagnosis framework demonstrate the accuracy and reliability of the methodology, as well as the availability of support for the stable operation of NPPs. This proposed comprehensive fault diagnosis framework is suitable for multi-dimensional monitoring of NPPs.