Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach-Reference-Cited by-同舟云学术

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Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach

Published:2024-09 Issue: Volume:174 Page:105287
ISSN:0149-1970
Container-title:Progress in Nuclear Energy
language:en
Short-container-title:Progress in Nuclear Energy

Author:

Liu Jie^ORCID,Zhang Qian,Macián-Juan Rafael

Publisher

Elsevier BV

Reference29 articles.

1. Explaining individual predictions when features are dependent: more accurate approximations to Shapley values;Aas;Artif. Intell.,2021

2. A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models;Chae;Ann. Nucl. Energy,2020

3. NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion;Chen;IEEE Trans. Ind. Electron.,2017

4. Learning phrase representations using RNN encoder-decoder for statistical machine translation;Cho;arXiv preprint arXiv:1406.1078,2014

5. RNN-based integrated system for real-time sensor fault detection and fault-informed accident diagnosis in nuclear power plant accidents;Choi;Nucl. Eng. Technol.,2023

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1. Improved versatility and robustness of bearing fault detection and diagnostic method for nuclear power plant;Nuclear Engineering and Design;2024-11

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