  During applying data-oriented diagnosis systems to distinguishing the type of
and evaluating the severity of nuclear power plant initial events, it is of
vital importance to decide which parameters to be used as the system input.
However, although several diagnosis systems have already achieved acceptable
performance in diagnosis precision and speed, hardly have the researchers
discussed the method of monitoring point choosing and its layout. For this
reason, redundant measuring data are used to train the diagnostic model,
leading to high uncertainty of the classification, extra training time
consumption, and higher probability of overfitting while training. In this
study, a method of choosing thermal hydraulics parameters of a nuclear power
plant is proposed, using the theory of post-hoc interpretability theory in deep
learning. At the start, a novel Time-sequential Residual Convolutional Neural
Network (TRES-CNN) diagnosis model is introduced to identify the position and
hydrodynamic diameter of breaks in LOCA, using 38 parameters manually chosen on
HPR1000 empirically. Afterwards, post-hoc interpretability methods are applied
to evaluate the attributions of diagnosis model's outputs, deciding which 15
parameters to be more decisive in diagnosing LOCA details. The results show
that the TRES-CNN based diagnostic model successfully predicts the position and
size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time
consumption while training the model compared the process using total 38
parameters. In addition, the relative diagnostic accuracy error is within 1.5
percent compared with the model using parameters chosen empirically, which can
be regarded as the same amount of diagnostic reliability.
