Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging

J Chen, J Li, R Huang, K Yue… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Generally, high performance of deep learning (DL)-based machinery fault diagnosis
methods relies on abundant labeled fault samples under various working conditions, while …

Artificial intelligence and edge computing for machine maintenance-review

A Bala, RZJA Rashid, I Ismail, D Oliva… - Artificial Intelligence …, 2024‏ - Springer
Industrial internet of things (IIoT) has ushered us into a world where most machine parts are
now embedded with sensors that collect data. This huge data reservoir has enhanced data …

Federated learning for condition monitoring of industrial processes: a review on fault diagnosis methods, challenges, and prospects

T Berghout, M Benbouzid, T Bentrcia, WH Lim, Y Amirat - Electronics, 2022‏ - mdpi.com
Condition monitoring (CM) of industrial processes is essential for reducing downtime and
increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling …

Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy

R Wang, W Huang, M Shi, J Wang, C Shen… - Knowledge-Based …, 2022‏ - Elsevier
Abstract Domain generalization (DG) methods have been successfully proposed to enhance
the generalization ability of the intelligent diagnosis model. However, these methods hardly …

An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms

L Wang, W Fan, G Jiang, P **e - Energy, 2023‏ - Elsevier
Wind turbine (WT) condition monitoring has gained increasing interests in the era of the
Internet of Energy (IoE), and existing monitoring approaches mainly focus on training a …

FedLED: Label-free equipment fault diagnosis with vertical federated transfer learning

J Shen, S Yang, C Zhao, X Ren, P Zhao… - IEEE Transactions …, 2024‏ - ieeexplore.ieee.org
Intelligent equipment fault diagnosis based on federated transfer learning (FTL) attracts
considerable attention from both academia and industry. It allows real-world industrial …

Intelligent fault diagnosis via ring-based decentralized federated transfer learning

L Wan, J Ning, Y Li, C Li, K Li - Knowledge-Based Systems, 2024‏ - Elsevier
Federated transfer learning (FTL) can effectively address the data silos and domain shift that
exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for …

[HTML][HTML] Clustering federated learning for bearing fault diagnosis in aerospace applications with a self-attention mechanism

W Li, W Yang, G **, J Chen, J Li, R Huang, Z Chen - Aerospace, 2022‏ - mdpi.com
Bearings, as the key mechanical components of rotary machinery, are widely used in
modern aerospace equipment, such as helicopters and aero-engines. Intelligent fault …

Federated domain generalization for intelligent fault diagnosis based on pseudo-siamese network and robust global model aggregation

Y Song, P Liu - International Journal of Machine Learning and …, 2024‏ - Springer
Federated learning (FL) based intelligent fault diagnosis has developed rapidly in recent
years owing to the need for data privacy. However, models trained using FL may suffer from …

Remaining useful life prediction of machinery using federated public feature representation in edge-cloud collaboration architecture

L Chen, H Gao, L Guo, J Liang, L Peng - Engineering Applications of …, 2025‏ - Elsevier
Significant progress has been made in the prediction methods of the remaining useful life
(RUL) of machinery. Nevertheless, two major challenges still exist for the large-scale …