A comparative study of deep learning-based fault diagnosis methods for rotating machines in nuclear power plants-Reference-Cited by-同舟云学术

A comparative study of deep learning-based fault diagnosis methods for rotating machines in nuclear power plants

Published:2022-12 Issue: Volume:178 Page:109334
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Qian Gensheng,Liu Jingquan

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference36 articles.

1. Bearing Data Center | Case School of Engineering | Case Western Reserve University. “[WWW Document], n.d.” https://engineering.case.edu/bearingdatacenter (July 2, 2022).

2. Diagnosis of Bearing Faults of Induction Machines by Vibration or Current Signals: A Critical Comparison;Bellini,2008

3. Learning Long-Term Dependencies with Gradient Descent Is Difficult;Bengio;IEEE Trans. Neural Netw.,1994

4. Cho, Kyunghyun, Bart van Merrienboer, Dzmitry Bahdanau, Yoshua Bengio, 2014. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches. In: SSST@EMNLP.

5. Convolutional Recurrent Neural Networks for Music Classification;Choi,2017

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples;Annals of Nuclear Energy;2024-05

2. Enhancing prediction accuracy for LOCA break sizes in nuclear power plants: A hybrid deep learning method with data augmentation and hyperparameter optimization;Annals of Nuclear Energy;2024-02

3. A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network;Journal of Vibroengineering;2023-11-04

4. Deep learning methods for neutron image restoration;Annals of Nuclear Energy;2023-08

5. A real-time unmeasured dynamic response prediction for nuclear facility pressure pipeline system;Nuclear Engineering and Technology;2023-07