Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions-Reference-Cited by-同舟云学术

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Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions

Published:2023-04 Issue: Volume:216 Page:119479
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Yan Xiaoan,She Daoming,Xu Yadong

Funder

North China Electric Power University

National Natural Science Foundation of China

Xi’an Jiaotong University

Natural Science Foundation of Jiangsu Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference71 articles.

1. Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data;An;Knowledge-Based Systems,2021

2. Visual inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling;Ardito;Expert Systems with Applications,2022

3. Dynamics-based peptide–MHC binding optimization by a convolutional variational autoencoder: A use-case model for CASTELO;Bell;Journal of Chemical Theory and Computation,2021

4. Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds;Cao;Journal of Manufacturing Systems,2022

5. Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders;Chao;Neurocomputing,2021

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