  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.
