Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant

Abstract

Safety is the most important indicator of a nuclear power plant. The extensive use of sensors could help operators gain more information about nuclear power plants. However, it also increases the risk of sensor failures. Therefore, it is necessary to study the multiple fault detection, isolation and reconstruction (FDIR) of sensors. For the traditional principal component analysis (PCA) model, this paper proposes two improvements. The first improvement is to propose a Corrected Reconstruction Algorithm (CRA) to improve the accuracy of the reconstruction. The traditional PCA reconstruction has lower accuracy when reconstructing multi-sensor faults. The second improvement is a cyclic PCA (CPCA) monitoring model to detect multi-sensor failures. The purpose of this is to improve the PCA model's ability to detect multiple sensor faults. Finally, the data from an actual nuclear power plant is used for modeling and verification. Simulation tests show that the CPCA model could accurately detect the different kinds of sensor faults and successfully reconstruct fault data.