Performance analysis and improvement of data-driven fault diagnosis models under domain discrepancy base on a small modular reactor

Dingyu Jiang, Hexin Wu, Junli Gou, Bo Zhang and Jianqiang Shan

Energy, 2025, vol. 316, issue C

Abstract: Data-driven fault diagnosis has attracted much attention in the field of nuclear energy. The complex operating environment of nuclear power plants tends to cause data to deviate from its own source domain, resulting in domain discrepancy thus performance degradation. So it is crucial to study the performance of the model under domain discrepancy. In this study, two evaluation metrics are proposed for domain discrepancy namely the generalization ability to fault severity and the robustness to data failures. Commonly used fault diagnosis models are built based on ACP100 reactor model, including simple Recurrent Neural Network (RNN), Long Short-Term Memory(LSTM), and Convolutional Neural Networks(CNN). The performance variations of these models are analyzed under the above domain discrepancy. The effect of noise is also analyzed in order to simulate real-world operations. In addition, the model performance is improved by Artificial Disturbance Methods, and the effects of different levels of model noise are analyzed. The LSTM model performs better than the other two models, according to the results under domain discrepancy. The maximum performance gain occurs when 10 dB of noise is introduced. These results provide a reference for the establishment and improvement of future fault diagnosis models for nuclear power plants.

Keywords: Fault diagnosis; Domain discrepancy; Fault severity; Data failure; Data-driven (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225001707
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:316:y:2025:i:c:s0360544225001707

DOI: 10.1016/j.energy.2025.134528

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().