Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder
This provides a robust and interpretable diagnostic method for nuclear reactor safety systems, addressing a critical domain-specific need.
This paper tackles reactor accident diagnosis by proposing a Denoising Padded Autoencoder (DPAE) that maintains effectiveness with noisy data (signal-to-noise ratios up to 25.0) and missing data (up to 40.0%), achieving 41.8% and 80.8% higher metrics in classification and regression tasks compared to end-to-end approaches.
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.