Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization

Abstract

A predictive approach to fault diagnosis in complex systems such as the Nuclear power plant (NPP) is becoming popular because of the efficiency and accuracy it presents. However, there is still a huge gap between the proposed fault diagnosis techniques and engineering applications. To further optimize the fault diagnosis route and encourage real-time application, this paper presents a highly accurate and adaptable fault diagnosis technique based on the convolutional gated recurrent unit (CGRU) and enhanced particle swarm optimization (EPSO). Stacking convolutional kernel and GRU results in a model that speedily extract the local characteristics and learn the time-series information. The EPSO is utilized to adaptively search for optimal hyper-parameters for the CGRU. Finally, the accuracy is evaluated on a dataset obtained from experiments, and comparative analysis of the proposed model with existing architectures and models are presented. Relevant research results that show the usefulness of the proposed model are also presented, which highlights the enhanced intelligence and information level achieved in the NPP fault diagnosis.