Integrating causal representations with domain adaptation for fault diagnosis DOI
Ming Jiang, Kuang Zhou, Jiahui Gao

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110999 - 110999

Published: March 1, 2025

Language: Английский

Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy DOI
Ke Zhao,

Junchen Hu,

Haidong Shao

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 236, P. 109246 - 109246

Published: March 21, 2023

Language: Английский

Citations

95
A novel data augmentation approach to fault diagnosis with class-imbalance problem DOI
Jilun Tian, Yuchen Jiang, Jiusi Zhang

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 243, P. 109832 - 109832

Published: Nov. 19, 2023

Language: Английский

Citations

57
Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing DOI
Lingli Cui, Yongchang Xiao, Dongdong Liu

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 245, P. 109991 - 109991

Published: Feb. 5, 2024

Language: Английский

Citations

52
Rolling Bearing Fault Diagnosis Under Data Imbalance and Variable Speed Based on Adaptive Clustering Weighted Oversampling DOI
Sai Li, Yanfeng Peng, Yiping Shen

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 244, P. 109938 - 109938

Published: Jan. 17, 2024

Language: Английский

Citations

51
Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization DOI
Shen Yan, Xiang Zhong, Haidong Shao

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 239, P. 109522 - 109522

Published: July 23, 2023

Language: Английский

Citations

47
A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern DOI
Pengcheng Xia, Yixiang Huang, Zhiyu Tao

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 235, P. 109256 - 109256

Published: March 21, 2023

Language: Английский

Citations

45
Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery DOI
Shengnan Tang, Jingtao Ma,

Zhengqi Yan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108678 - 108678

Published: June 3, 2024

Language: Английский

Citations

32
Extended attention signal transformer with adaptive class imbalance loss for Long-tailed intelligent fault diagnosis of rotating machinery DOI
Shuyuan Chang, Liyong Wang, Mingkuan Shi

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102436 - 102436

Published: Feb. 29, 2024

Language: Английский

Citations

20
A Novel Transformer-based Few-Shot Learning Method for Intelligent Fault Diagnosis with Noisy Labels under Varying Working Conditions DOI
Haoyu Wang, Chuanjiang Li, Peng Ding

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 251, P. 110400 - 110400

Published: July 31, 2024

Language: Английский

Citations

20
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review DOI Creative Commons
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102700 - 102700

Published: Aug. 10, 2024

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result complete system failure, it accounts for approximately 45 %–50 % failures rotating machinery. Hence, is imperative establish thorough accurate predictive maintenance program that can efficiently foresee prevent mishaps or malfunctions. The literature employed variety techniques approaches, from conventional methods contemporary machine learning (ML) ML-integrated IoT-based solutions, categorise bearing faults. This article provides an overview most recent research models used classification summary highlights various significant challenges current models, issues with function, complexities neural network structure, unrealistic datasets, dynamic working conditions machines, noise dataset, limited data availability, imbalanced datasets. In order tackle problems, researchers have endeavored improve apply different methods, convolutional networks, deep belief LiNet, among others. Researchers primarily developed these approaches using datasets publicly accessible sources. study also identified gaps deficiencies, including imbalance, difficulties integration. nascent technologies field problem diagnosis acknowledged Internet Things-based ML vision-based techniques, which currently their initial phases advancement. Ultimately, puts forth several prospective suggestions recommendations.

Language: Английский

Citations

19