[HTML][HTML] Machine Fault Diagnosis: Experiments with Different Attention Mechanisms Using a Lightweight SqueezeNet Architecture
As artificial intelligence technology progresses, deep learning models are increasingly
utilized for machine fault classification. However, a significant drawback of current state-of …
utilized for machine fault classification. However, a significant drawback of current state-of …
A Siamese CNN-BiLSTM-based method for unbalance few-shot fault diagnosis of rolling bearings
X Liu, G Chen, H Wang, X Wei - Measurement and Control, 2024 - journals.sagepub.com
Small and imbalanced fault samples have a profound impact on the diagnostic performance
of a model in the process of locating and quantifying the rolling bearing damage of …
of a model in the process of locating and quantifying the rolling bearing damage of …
Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier
We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture
classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of …
classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of …
[HTML][HTML] From Domain Adaptation to Federated Learning
Z Taghiyarrenani - 2024 - diva-portal.org
Data-driven methods have been gaining increasing attention; however, along with the
benefits they offer, they also present several challenges, particularly concerning data …
benefits they offer, they also present several challenges, particularly concerning data …