A novel transfer CNN with spatiotemporal input for accurate nuclear power fault diagnosis under different operating conditions

Abstract

Deep Neural Network (DNN) models, recognized for their exceptional feature extraction and end-to-end diagnostic capabilities, have gained considerable attention from researchers in the realm of nuclear power fault diagnosis. However, existing deep-learning based diagnostic technologies present certain challenges when applied to practical engineering scenarios. A significant issue is the limited adaptability of data-based machine learning diagnostic methods to dynamic and evolving operational conditions, leading to inconsistent accuracy under different operating parameters. To solve this problem, a spatiotemporal Convolutional Neural Network (CNN) model is proposed. A novel input scheme, considering both spatial and temporal aspects, is specially designed for nuclear power plant fault diagnosis and MLP convolutional layer are used to improve the feature extraction capability of the model. Finally, the performance of the model is analyzed through a series of experiments based on simulation data and data visualization. The results show that the proposed method outperforms traditional CNNs in diagnosing nuclear power system faults with higher accuracy. Furthermore, by employing a transfer learning strategy, the model exhibits improved speed for cross-operating condition fault diagnosis.