Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network

TL;DR

This paper proposes a hierarchical multi-granularity classification network based on EfficientNet for fault diagnosis in nuclear power systems, utilizing simulated fault data to improve the analysis of complex system faults.

Contribution

It introduces a novel hierarchical classification model tailored for nuclear power fault diagnosis, addressing the limitations of existing single-device methods.

Findings

Effective hierarchical classification of faults in nuclear power units

Model achieves high accuracy in classifying faults across circuits and components

Simulated dataset demonstrates the model's potential for real-world applications

Abstract

The safe and reliable operation of complex electromechanical systems in nuclear power plants is crucial for the safe production of nuclear power plants and their nuclear power unit. Therefore, accurate and timely fault diagnosis of nuclear power systems is of great significance for ensuring the safe and reliable operation of nuclear power plants. The existing fault diagnosis methods mainly target a single device or subsystem, making it difficult to analyze the inherent connections and mutual effects between different types of faults at the entire unit level. This article uses the AP1000 full-scale simulator to simulate the important mechanical component failures of some key systems in the primary and secondary circuits of nuclear power units, and constructs a fault dataset. Meanwhile, a hierarchical multi granularity classification fault diagnosis model based on the EfficientNet large model is proposed, aiming to achieve hierarchical classification of nuclear power faults. The results indicate that the proposed fault diagnosis model can effectively classify faults in different circuits and system components of nuclear power units into hierarchical categories. However, the fault dataset in this study was obtained from a simulator, which may introduce additional information due to parameter redundancy, thereby affecting the diagnostic performance of the model.