With the mass construction of Gen III nuclear reactors, it is a popular trend to use deep learning (DL) techniques for fast and effective diagnosis of possible accidents. To overcome the common problems of previous work in diagnosing reactor accidents using deep learning theory, this paper proposes a diagnostic process that ensures robustness to noisy and crippled data and is interpretable. First, a novel Denoising Padded Autoencoder (DPAE) is proposed for representation extraction of monitoring data, with representation extractor still effective on disturbed data with signal-to-noise ratios up to 25.0 and monitoring data missing up to 40.0%. Secondly, a diagnostic framework using DPAE encoder for extraction of representations followed by shallow statistical learning algorithms is proposed, and such stepwise diagnostic approach is tested on disturbed datasets with 41.8% and 80.8% higher classification and regression task evaluation metrics, in comparison with the end-to-end diagnostic approaches. Finally, a hierarchical interpretation algorithm using SHAP and feature ablation is presented to analyze the importance of the input monitoring parameters and validate the effectiveness of the high importance parameters. The outcomes of this study provide a referential method for building robust and interpretable intelligent reactor anomaly diagnosis systems in scenarios with high safety requirements.
翻译:随着Gen III核反应堆的大规模建设,使用深学习(DL)技术快速有效地诊断可能发生的事故是一个流行的趋势。为了克服先前利用深学习理论对反应堆事故进行诊断的常见问题,本文件建议了一种诊断过程,以确保对噪音和残缺的数据的稳健性,并且可以解释。首先,提议了一个新的Denoising Padd Aut Autoencoder(DPAE)来代表对监测数据进行提取,代表提取器仍然对信号到噪音比率高达25.0的干扰数据有效,监测数据缺失达40.0%的数据。第二,提出了使用DPAE编码器提取表解的诊断框架,并随后采用浅统计学习算法,这种渐进式诊断方法的检测方法是在与端对端诊断方法相比,以41.8%和80.8%的更高分类和回归任务评价尺度对被破坏的数据集进行测试。最后,提出了使用SHAMP和特征校准的等级解释算法,以分析输入监测参数的重要性,并验证高重要性参数的有效性。第二,提出了使用DA型分析结果,提供了高清晰的模型分析要求。