Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization. (February 2021)

• Record Type: Journal Article
• Authors: Wang, Hang; Peng, Min-jun; Ayodeji, Abiodun; Xia, Hong; Wang, Xiao-kun; Li, Zi-kang
• Is Part Of: Annals of nuclear energy. Volume 151(2021)
• Publication Date: 2021-02
• Subjects: Fault diagnosis -- Deep learning -- GRU network -- Convolutional kernel -- Particle swarm optimization; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.107934
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The technical framework of digital twin model, deep learning and heuristic algorithm is established. Convolution kernel and GRU network are combined to achieve a better results. EPSO was used for adaptive optimization of CGRU, which could enhance accuracy and stability. 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.