Imbalanced fault diagnosis of a conditional variational auto-encoder with transfer and adversarial structures DOI

Xiangkun Zhao,

Xiaomin Zhu, Runtong Zhang

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103032 - 103032

Published: Dec. 12, 2024

Language: Английский

FD-LLM: Large language model for fault diagnosis of complex equipment DOI
Lin Lin,

Sihao Zhang,

Fu Song

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103208 - 103208

Published: Feb. 18, 2025

Citations

1
Denoising diffusion probabilistic model-enabled data augmentation method for intelligent machine fault diagnosis DOI

Pengcheng Zhao,

Zhang We,

Xiaoshan Cao

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109520 - 109520

Published: Oct. 22, 2024

Citations

4
Fault diagnosis of high-speed train suspension systems under variable speeds based on dynamic transfer loss weight-deep subdomain adaptation network DOI
Funing Yang, Chunrong Hua,

Junyi Mu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 64, P. 103091 - 103091

Published: Jan. 1, 2025

Language: Английский

Citations

0
Auxiliary-feature-embedded causality-inspired dynamic penalty networks for open-set domain generalization diagnosis scenario DOI
Ning Jia,

Weiguo Huang,

Chuancang Ding

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103220 - 103220

Published: Feb. 24, 2025

Language: Английский

Citations

0
Feature Similarity-Aware Open-Set Fault Diagnosis Via an Adaptive Dual-Stage Recognition Framework DOI
Penglong Lian, Zhiheng Su, Penghui Shang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning DOI Creative Commons

Tongfei Lei,

Feng Pan,

Jiabei Hu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

The closed-set assumption often fails in practical industrial applications, especially considering diverse working conditions where the data distribution may differ significantly. In light of this, a domain adaptation method with adversarial learning is designed for open-set fault diagnosis. Firstly, convolutional autoencoder developed to distill features; Secondly, an unknown boundary by weighting similarity between known and classes established, ensure shared class alignment domains while classifying across identifying samples. Finally, diagnostic performance evaluated using three sets rolling bearing datasets. proposed achieved average F1-scores 96.60%, 96.56%, 96.62% on these datasets, respectively. results demonstrate that effectively rejects target aligning classes, validating its diagnosis capability under open-world assumption.

Language: Английский

Citations

0
Dynamic Meta-Decoupler-inspired Single-Universal Domain Generalization for Intelligent Fault Diagnosis DOI
Minrui Xu, Yingjie Zhang, Biliang Lu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 282, P. 127528 - 127528

Published: April 21, 2025

Language: Английский

Citations

0
A two-stage learning framework for imbalanced semi-supervised domain generalization fault diagnosis under unknown operating conditions DOI
Chuanxia Jian, Heen Chen, Yinhui Ao

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102878 - 102878

Published: Oct. 1, 2024

Language: Английский

Citations

2
Imbalanced fault diagnosis of a conditional variational auto-encoder with transfer and adversarial structures DOI

Xiangkun Zhao,

Xiaomin Zhu, Runtong Zhang

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103032 - 103032

Published: Dec. 12, 2024

Language: Английский

Citations

0