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<p>Safety is of paramount importance in nuclear power plants. Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems. The convergence of Industry 4.0 technologies and deep learning methods has emerged as a promising approach for improving the operational safety of nuclear energy systems, particularly in fault detection and diagnosis (FDD) applications. This study proposes a novel adaptive accident diagnosis framework tailored for molten salt reactors (MSRs) based on an enhanced residual convolutional neural network (AM-RCNN). The AM-RCNN incorporates an anti-noise module implemented using the soft thresholding method, together with an attention mechanism, to improve robustness. Datasets representing eight distinct operational scenarios were generated using the RELAP5-TMSR simulation tool. An appropriate subset of input features for MSR accident diagnosis was selected using Pearson correlation analysis and random forest importance ranking. The models were subsequently trained, validated, optimized, and tested. Comparative analyses with conventional RCNN and CNN architectures demonstrate the diagnostic advantages of the proposed approach. In addition, the integration of Bayesian optimization further enhances the performance of the AM-RCNN. As a contribution to intelligent monitoring research for MSRs, the proposed method provides reliable decision support for nuclear system operation, particularly in autonomous scenarios.</p>

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Enhanced accident diagnosis for molten salt reactors using an adaptive residual CNN with Bayesian optimization

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Chao-Qun Wang,
  • Kai Wang,
  • Qun Yang,
  • Rong-Jian Liang,
  • Xin Yue,
  • Liang-Jie Sun,
  • Wen-Tao Jiang,
  • Zhao-Zhong He,
  • Na-Xiu Wang

摘要

Safety is of paramount importance in nuclear power plants. Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems. The convergence of Industry 4.0 technologies and deep learning methods has emerged as a promising approach for improving the operational safety of nuclear energy systems, particularly in fault detection and diagnosis (FDD) applications. This study proposes a novel adaptive accident diagnosis framework tailored for molten salt reactors (MSRs) based on an enhanced residual convolutional neural network (AM-RCNN). The AM-RCNN incorporates an anti-noise module implemented using the soft thresholding method, together with an attention mechanism, to improve robustness. Datasets representing eight distinct operational scenarios were generated using the RELAP5-TMSR simulation tool. An appropriate subset of input features for MSR accident diagnosis was selected using Pearson correlation analysis and random forest importance ranking. The models were subsequently trained, validated, optimized, and tested. Comparative analyses with conventional RCNN and CNN architectures demonstrate the diagnostic advantages of the proposed approach. In addition, the integration of Bayesian optimization further enhances the performance of the AM-RCNN. As a contribution to intelligent monitoring research for MSRs, the proposed method provides reliable decision support for nuclear system operation, particularly in autonomous scenarios.