A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types-Reference-Cited by-同舟云学术

A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types

Published:2024-08 Issue: Volume:61 Page:102469
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

He Zhenzhong,Shen Changqing,Chen Bojian,Shi Juanjuan,Huang Weiguo,Zhu Zhongkui,Wang Dong

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference30 articles.

1. Time-frequency supervised contrastive learning via pseudo-labeling: an unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds;Pang;Adv. Eng. Inf.,2024

2. Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions;Wang;IEEE Trans. Instrum. Meas.,2022

3. A lifelong learning method based on generative feature replay for bearing diagnosis with incremental fault types;Liu;IEEE Trans. Instrum. Meas.,2023

4. Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed fluctuation and extremely low labeled rates;Yan;Adv. Eng. Inf.,2022

5. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine;Han;Inf. Sci.,2023

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