Fractional derivative kernel recursive generalized maximum correntropy for RUL prediction of rolling bearings-Reference-Cited by-同舟云学术

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Fractional derivative kernel recursive generalized maximum correntropy for RUL prediction of rolling bearings

Published:2024-08 Issue: Volume:217 Page:111527
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Zhang Tingsen^ORCID,Ye Ming^ORCID,Li Xifeng^ORCID,Bi Dongjie^ORCID,Peng Libiao^ORCID,Xie Yongle^ORCID

Funder

National Natural Science Foundation of China

Yibin Science and Technology Planning Program

Publisher

Elsevier BV

Reference57 articles.

1. Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method;Chen;Reliab. Eng. Syst. Saf.,2023

2. Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings;Yang;Reliab. Eng. Syst. Saf.,2023

3. CMAFI — Copula-based multifeature autocorrelation fault identification of rolling bearing;Duda;Mech. Syst. Signal Process.,2024

4. RUL prediction for rolling bearings based on convolutional autoencoder and status degradation model;Weiyang;Appl. Soft Comput.,2022

5. Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings;Shilong;Reliab. Eng. Syst. Saf.,2024

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