Dual disentanglement domain generalization method for rotating Machinery fault diagnosis DOI
Guowei Zhang, Xianguang Kong, Hongbo Ma

et al.

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 228, P. 112460 - 112460

Published: Feb. 14, 2025

Language: Английский

A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions DOI
Tianyu Gao, Jingli Yang,

Wenmin Wang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 252, P. 110449 - 110449

Published: Aug. 22, 2024

Language: Английский

Citations

22
SDCGAN: A CycleGAN-Based Single-Domain Generalization Method for Mechanical Fault Diagnosis DOI
Yu Guo, Xiangyu Li, Jundong Zhang

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110854 - 110854

Published: Jan. 1, 2025

Language: Английский

Citations

3
DP2Net: A discontinuous physical property-constrained single-source domain generalization network for tool wear state recognition DOI
Xuwei Lai, Kai Zhang,

Qing Zheng

et al.

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 215, P. 111421 - 111421

Published: April 15, 2024

Language: Английский

Citations

18
Single-domain incremental generation network for machinery intelligent fault diagnosis under unknown working speeds DOI

Yuanyue Pu,

Jian Tang, Xue‐Gang Li

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102400 - 102400

Published: Feb. 17, 2024

Language: Английский

Citations

17
A Novel Reinforcement Learning Agent for Rotating Machinery Fault Diagnosis with Data Augmentation DOI
Zhenning Li, Hongkai Jiang, Xin Wang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 253, P. 110570 - 110570

Published: Oct. 6, 2024

Language: Английский

Citations

13
Domain generalization for rotating machinery fault diagnosis: A survey DOI
Yiming Xiao, Haidong Shao, Shen Yan

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103063 - 103063

Published: Dec. 19, 2024

Language: Английский

Citations

11
Dual adversarial and contrastive network for single-source domain generalization in fault diagnosis DOI

Guangqiang Li,

Mohamed Amine Atoui,

Xiangshun Li

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103140 - 103140

Published: Feb. 3, 2025

Language: Английский

Citations

2
Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples DOI
Feiyu Lu, Qingbin Tong,

Xuedong Jiang

et al.

Computers in Industry, Journal Year: 2024, Volume and Issue: 164, P. 104169 - 104169

Published: Sept. 7, 2024

Language: Английский

Citations

8
Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions DOI
Chuanxia Jian, Heen Chen, Chaobin Zhong

et al.

Structural Health Monitoring, Journal Year: 2024, Volume and Issue: 23(6), P. 3904 - 3920

Published: Feb. 28, 2024

Equipment operating conditions, referred to as domains, can induce domain drift in monitoring data, affecting data-driven fault diagnosis. Researchers have explored multi-domain generalization methods tackle this issue. However, actual industrial scenarios, the availability of data may be limited a specific condition due cost or feasibility constraints associated with collecting extensive data. This limitation hampers ability these methods, posing major challenge for robust diagnosis under variable conditions. To address challenge, we proposed gradient-based domain-augmented meta-learning (GDM) single-domain method. We analyze restrictions generating fake domains and construct loss by evaluating diagnostic tasks minimization, semantic consistency, distribution diversity samples. Using technique, are generated iteratively, providing diverse knowledge improved generalization. Instead using time-consuming ensemble develop novel method train highly efficient generalizable model, relaxing requirement auxiliary datasets existing methods. Two case studies consistently demonstrate effectiveness superiority GDM Our findings suggest that study offers promising competitive solution within real scenarios.

Language: Английский

Citations

7
Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis DOI
Guowei Zhang, Xianguang Kong, Qibin Wang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 250, P. 110252 - 110252

Published: June 8, 2024

Language: Английский

Citations

7