A Semisupervised Method With Swarm Intelligence Optimization for Intelligent Fault Diagnosis-Reference-Cited by-同舟云学术

A Semisupervised Method With Swarm Intelligence Optimization for Intelligent Fault Diagnosis

Published:2023-06-01 Issue:11 Volume:23 Page:11968-11977
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Xu Zhiming¹^ORCID,Feng Zhixi¹^ORCID,Wu Qiang¹,Yang Shuyuan¹^ORCID

Affiliation:

1. Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education and the School of Artificial Intelligence, Xidian University, Xi’an, China

Funder

National Natural Science Foundation of China

Science and Technology Innovation Team in Shaanxi Province of China

Foundation of Intelligent Decision and Cognitive Innovation Center of State Administration of Science, Technology and Industry for National Defense, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10139852/10106777.pdf?arnumber=10106777

Reference41 articles.

1. Semi-supervised graph convolutional network and its application in intelligent fault diagnosis of rotating machinery

2. Sequential Fault Diagnosis Based on LSTM Neural Network

3. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning

4. Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis

5. Anomaly Detection of Disconnects Using SSTDR and Variational Autoencoders

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