Meta-learning based voltage control strategy for emergency faults of active distribution networks
With the increase of energy demand and the continuous development of renewable energy
technology, active distribution networks have become increasingly important. However, the …
technology, active distribution networks have become increasingly important. However, the …
Few-shot learning approaches for fault diagnosis using vibration data: a comprehensive review
Fault detection and diagnosis play a crucial role in ensuring the reliability and safety of
modern industrial systems. For safety and cost considerations, critical equipment and …
modern industrial systems. For safety and cost considerations, critical equipment and …
[HTML][HTML] State of health estimation for lithium battery random charging process based on CNN-GRU method
The accurate estimation of lithium battery state of health (SOH) is very important for the safe
and stable operation of the battery. Since the user's charging process is random, it is difficult …
and stable operation of the battery. Since the user's charging process is random, it is difficult …
Fault detection in wind turbine generators using a meta-learning-based convolutional neural network
L Qiao, Y Zhang, Q Wang - Mechanical Systems and Signal Processing, 2023 - Elsevier
Conventional fault detection methods for wind turbine (WT) generators often grapple with
inadequate warning times and poor portability. These issues contribute to heightened safety …
inadequate warning times and poor portability. These issues contribute to heightened safety …
Meta-learning based voltage control for renewable energy integrated active distribution network against topology change
This letter presents a meta-learning based voltage control strategy for renewable energy
integrated active distribution network. The multiple interference self-supervised method is …
integrated active distribution network. The multiple interference self-supervised method is …
Domain discrepancy-guided contrastive feature learning for few-shot industrial fault diagnosis under variable working conditions
Recent advances in data-driven methods have significantly promoted intelligent fault
diagnostics for varied industrial applications. However, due to the limitations of machine fault …
diagnostics for varied industrial applications. However, due to the limitations of machine fault …
Small data challenges for intelligent prognostics and health management: a review
Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …
and reducing maintenance costs, and research on intelligent PHM has made significant …
Dual prototypical contrastive network: a novel self-supervised method for cross-domain few-shot fault diagnosis
Data-driven methods have pushed mechanical fault diagnostics to an unprecedented height
recently. However, their satisfactory performance heavily relies on the availability of …
recently. However, their satisfactory performance heavily relies on the availability of …
[HTML][HTML] A novel framework based on adaptive multi-task learning for bearing fault diagnosis
Bearing fault diagnosis is very important for the security and efficiency of electric machines.
In recent years, the newly emerging deep learning methods have risen bearing fault …
In recent years, the newly emerging deep learning methods have risen bearing fault …
Meta-Learning With Distributional Similarity Preference for Few-Shot Fault Diagnosis Under Varying Working Conditions
Few-shot fault diagnosis is a challenging problem for complex engineering systems due to
the shortage of enough annotated failure samples. This problem is increased by varying …
the shortage of enough annotated failure samples. This problem is increased by varying …