A survey on federated learning and its applications for accelerating industrial internet of things
Federated learning (FL) brings collaborative intelligence into industries without centralized
training data to accelerate the process of Industry 4.0 on the edge computing level. FL …
training data to accelerate the process of Industry 4.0 on the edge computing level. FL …
Transferable graph features-driven cross-domain rotating machinery fault diagnosis
Graph data has been integrated into transfer learning-based cross-domain rotating
machinery diagnosis for reducing domain discrepancy. Sample relationships, representing …
machinery diagnosis for reducing domain discrepancy. Sample relationships, representing …
Definition of a novel federated learning approach to reduce communication costs
G Paragliola, A Coronato - Expert Systems with Applications, 2022 - Elsevier
Abstract Background and Objective: Contemporary Machine Learning approaches (eg,
Deep Learning) need huge volumes of data to build accurate and robust statistical models …
Deep Learning) need huge volumes of data to build accurate and robust statistical models …
Dynamic normalization supervised contrastive network with multiscale compound attention mechanism for gearbox imbalanced fault diagnosis
Y Dong, H Jiang, W Jiang, L Xie - Engineering Applications of Artificial …, 2024 - Elsevier
Deep learning has gained significant success in fault diagnosis. However, the number of
gearbox health samples is inevitably much larger than that of fault samples in real-world …
gearbox health samples is inevitably much larger than that of fault samples in real-world …
A personalized federated learning-based fault diagnosis method for data suffering from network attacks
Z Zhang, F Zhou, C Zhang, C Wen, X Hu, T Wang - Applied Intelligence, 2023 - Springer
Federated learning (FL) is an effective way to incorporate information provided by different
clients when a single local client is unable to provide sufficient training samples for …
clients when a single local client is unable to provide sufficient training samples for …
[HTML][HTML] A transfer learning framework with a one-dimensional deep subdomain adaptation network for bearing fault diagnosis under different working conditions
R Zhang, Y Gu - Sensors, 2022 - mdpi.com
Accurate and fast rolling bearing fault diagnosis is required for the normal operation of
rotating machinery and equipment. Although deep learning methods have achieved …
rotating machinery and equipment. Although deep learning methods have achieved …
Fuzzy consensus with federated learning method in medical systems
D Połap - IEEE Access, 2021 - ieeexplore.ieee.org
Large-scale group decision-making (LSGDM) is one of the main open problems where a
decision is made by many different results. Moreover, there is also a problem with how to …
decision is made by many different results. Moreover, there is also a problem with how to …
Industrial edge intelligence: Federated-meta learning framework for few-shot fault diagnosis
The scarcity of fault samples has been the bottleneck for the large-scale application of
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …
Mix-VAEs: A novel multisensor information fusion model for intelligent fault diagnosis
C Wang, C Xin, Z Xu, M Qin, M He - Neurocomputing, 2022 - Elsevier
Multisensor information are usually required to recognize the health condition of machinery
by domain experts, since redundancy and complementarity of multisensor information can …
by domain experts, since redundancy and complementarity of multisensor information can …
Hybrid machine condition monitoring based on interpretable dual tree methods using Wasserstein metrics
For condition monitoring and predictive maintenance of high-end manufacturing equipment,
surface roughness is a critical metric to evaluate machining quality. Designing a method that …
surface roughness is a critical metric to evaluate machining quality. Designing a method that …