Dynamic weighted federated remaining useful life prediction approach for rotating machinery
In actual industrial scenarios, the centralized learning paradigm for remaining useful life
(RUL) prediction of rotating machineries usually suffers from several bottlenecks. Firstly, the …
(RUL) prediction of rotating machineries usually suffers from several bottlenecks. Firstly, the …
DeepFedWT: A federated deep learning framework for fault detection of wind turbines
Data-driven fault detection of wind turbines has gained increasingly attention. Currently,
most existing methods require sufficient labeled data to train a reliable model in a …
most existing methods require sufficient labeled data to train a reliable model in a …
Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction
Predictive maintenance (PdM) plays an important role in industrial manufacturing. One of the
most fundamental ideas underlying many PdM solutions is to estimate Remaining Useful …
most fundamental ideas underlying many PdM solutions is to estimate Remaining Useful …
Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning
In recent years, deep neural networks have been widely applied in remaining useful life
(RUL) prediction, and good prognostic performance has been achieved. However, existing …
(RUL) prediction, and good prognostic performance has been achieved. However, existing …
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 …
exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for …
Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy
Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …
years, and promising diagnostic performance has been achieved with sufficient monitoring …
A federated learning approach to mixed fault diagnosis in rotating machinery
Rotating machinery is ubiquitous in modern industrial systems. Ensuring optimal operating
conditions for rotating machinery is essential to satisfy stringent requirements on safety …
conditions for rotating machinery is essential to satisfy stringent requirements on safety …
[HTML][HTML] Smart and collaborative industrial IoT: A federated learning and data space approach
B Farahani, AK Monsefi - Digital Communications and Networks, 2023 - Elsevier
Industry 4.0 has become a reality by fusing the Industrial Internet of Things (IIoT) and
Artificial Intelligence (AI), providing huge opportunities in the way manufacturing companies …
Artificial Intelligence (AI), providing huge opportunities in the way manufacturing companies …
A domain adaptation method for bearing fault diagnosis using multiple incomplete source data
Q Wang, Y Xu, S Yang, J Chang, J Zhang… - Journal of Intelligent …, 2024 - Springer
The fault diagnosis method based on domain adaptation is a hot topic in recent years. It is
difficult to collect a complete data set containing all fault categories in practice under the …
difficult to collect a complete data set containing all fault categories in practice under the …
Privacy-preserved generative network for trustworthy anomaly detection in smart grids: A federated semisupervised approach
M Abdel-Basset, N Moustafa… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
The deep integration of industrial internet of things technologies in the industrial smart grid
(ISG) brings many privacy and security attacks, threatening the trustworthiness of underlying …
(ISG) brings many privacy and security attacks, threatening the trustworthiness of underlying …