An Unsupervised Pairwise Comparison Learning Approach With Adaptive Network Structure for Equipment Health Quantitative Assessment-Reference-Cited by-同舟云学术

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An Unsupervised Pairwise Comparison Learning Approach With Adaptive Network Structure for Equipment Health Quantitative Assessment

Published:2023-06-01 Issue:11 Volume:23 Page:11978-11991
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhao Juanru¹,Yuan Mei²,Cui Jin³^ORCID,Dong Shaopeng²,Mei Shuaijie²

Affiliation:

1. School of Automation Science and Electrical Engineering and the Sino-French Engineer School, Beihang University, Beijing, China

2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

3. Research Institute for Frontier Science, Beihang University, Beijing, China

Funder

Beijing Natural Science Foundation

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10139852/10107691.pdf?arnumber=10107691

Reference39 articles.

1. Machinery condition monitoring in the era of industry 4.0: A relative degree of contribution feature selection and deep residual network combined approach

2. Pairwise comparison learning based bearing health quantitative modeling and its application in service life prediction

3. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks

4. ContrastNet: Unsupervised feature learning by autoencoder and prototypical contrastive learning for hyperspectral imagery classification

5. Application of Convolutional Neural Network and Data Preprocessing by Mutual Dimensionless and Similar Gram Matrix in Fault Diagnosis

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3. A weakly supervised pairwise comparison learning approach for bearing health quantitative evaluation and remaining useful life prediction;Engineering Computations;2023-08-16

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