  With the increasing use of high-precision system analysis programs in nuclear
engineering, the number of high-fidelity computational data for accident
simulation is exploding. Therefore, an algorithm that can achieve both
automatic extraction of low-dimensional features from the data and guarantee
the validity of the features is needed to improve the performance and
confidence of the accident diagnosis system. This study proposes an
autoencoder-based autonomous learning framework, namely Padded Auto-Encoder
(PAE), which is able to automatically encode accident monitoring data that has
been noise-added and with partially missing data into low-dimensional feature
vectors via a Vision Transformer-based encoder, and to decode the feature
vectors into noise-free and complete reconstructed monitoring data. Thus, the
encoder part of the framework is able to automatically infer valid
representations from partially missing and noisy monitoring data that reflect
the complete and noise-free original data, and the representation vectors can
be used for downstream tasks for accident diagnosis or else. In this paper,
LOCA of HPR1000 was used as the study object, and the PAE was trained by an
unsupervised method using cases with different break locations and sizes as the
dataset. The encoder part of the pre-trained PAE was subsequently used as the
feature extractor for the monitoring data, and several basic statistical
learning algorithms for predicting the break locations and sizes. The results
of the study show that the pre-trained diagnostic model with two stages has a
better performance in break location and size diagnostic capability with an
improvement of 41.62% and 80.86% in the metrics respectively, compared to the
diagnostic model with end-to-end model structure.
