Health state identification of circulating seawater pump-unit in nuclear power plant based on multi-virtual vibration source fusion in the presence of strong data imbalance

Abstract

The circulating seawater pump-unit (CRF pump-unit) is a crucial equipment that provides cooling water for the secondary circuit of a nuclear power plant (NPP). Its long-term, fault-free operation ensures nuclear power safety and economy. In this study, considering the limited fault-state samples in the historical database and the random fault locations in NPP equipment, a health state identification framework of the CRF pump-unit is proposed, which combines multiple sources of network input and a feature fusion network. The proposed kernelled independent component analysis with reference (KICA-R) is utilized with reference signals to separate virtual sources from multiple raw acceleration signals. It enhances the fault characteristic frequencies related to several subsystems of a complex mechatronic system in the training samples of the network. Based on the newly designed multi-source channel attention module (MS-CAM) that can adaptively allocate fault-sensitive weight coefficients, a health state identification network incorporates the enhanced input features through the virtual sources obtained from KICA-R. By conducting the benchmark analysis on two public datasets and various types of fault simulation tests on the CRF pump-unit simulator, training sets with different levels of data imbalance are constructed, and the optimal network parameters are determined. The comparisons against cICA validate the ability of KICA-R in virtual source separation, and the strong performance of the MS-CAM-based network in identifying different health states of the CRF pump-unit is further demonstrated through detailed comparative analysis.