Deep Learning-Based Composite Fault Diagnosis-Reference-Cited by-同舟云学术

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Deep Learning-Based Composite Fault Diagnosis

Published:2023-06 Issue:2 Volume:13 Page:572-581
ISSN:2156-3357
Container-title:IEEE Journal on Emerging and Selected Topics in Circuits and Systems
language:
Short-container-title:IEEE J. Emerg. Sel. Topics Circuits Syst.

Author:

An Zining¹^ORCID,Wu Fan¹^ORCID,Zhang Cong¹^ORCID,Ma Jinhao¹,Sun Bo¹,Tang Bihua¹^ORCID,Liu Yuanan¹^ORCID

Affiliation:

1. Beijing University of Posts and Telecommunications, Beijing, China

Funder

Beijing Natural Science Foundation Project

National Natural Science Foundation of China

Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/5503868/10149835/10083040.pdf?arnumber=10083040

Reference25 articles.

1. Fault State Recognition of Rolling Bearing Based Fully Convolutional Network

2. An improved bearing fault diagnosis method using one-dimensional CNN and LSTM;pan;J Mech Eng,2018

3. Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network

4. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals

5. Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

Cited by 3 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 Transferability-Enhanced and Structural Information Mining Network for Cross-Domain Fault Diagnosis;IEEE Sensors Journal;2024-07-01

2. Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis;Applied Sciences;2024-03-05

3. Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey;Open Physics;2024-01-01

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