Meta-learning for few-shot bearing fault diagnosis under complex working conditions
Deep learning-based bearing fault diagnosis has been systematically studied in recent
years. However, the success of most of these methods relies heavily on massive labeled …
years. However, the success of most of these methods relies heavily on massive labeled …
Few-shot bearing fault diagnosis based on model-agnostic meta-learning
The rapid development of artificial intelligence and deep learning has provided many
opportunities to further enhance the safety, stability, and accuracy of industrial cyber …
opportunities to further enhance the safety, stability, and accuracy of industrial cyber …
Few-shot cross-domain fault diagnosis of bearing driven by task-supervised ANIL
Meta-learning has effectively addressed the limit of deep learning fault diagnosis models
that demands a large number of samples. However, existing meta-learning models lack the …
that demands a large number of samples. However, existing meta-learning models lack the …
Few-shot rolling bearing fault diagnosis with metric-based meta learning
S Wang, D Wang, D Kong, J Wang, W Li, S Zhou - Sensors, 2020 - mdpi.com
Fault diagnosis methods based on deep learning and big data have achieved good results
on rotating machinery. However, the conventional deep learning method of bearing fault …
on rotating machinery. However, the conventional deep learning method of bearing fault …
Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions
The real-world large industry has gradually become a data-rich environment with the
development of information and sensor technology, making the technology of data-driven …
development of information and sensor technology, making the technology of data-driven …
Limited data rolling bearing fault diagnosis with few-shot learning
This paper focuses on bearing fault diagnosis with limited training data. A major challenge in
fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type …
fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type …
Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning
C Che, H Wang, M Xiong, X Ni - Digital Signal Processing, 2022 - Elsevier
Accurate fault diagnosis of rolling bearing under variable working conditions can ensure that
the rotating machinery run in a safety, reliable and efficient way. In this paper, we propose …
the rotating machinery run in a safety, reliable and efficient way. In this paper, we propose …
A meta-learning method for electric machine bearing fault diagnosis under varying working conditions with limited data
Effective detection of fault in rolling bearings with a limited amount of data is essential for the
safe operation of electric machines. This article proposes a novel meta-learning-enabled …
safe operation of electric machines. This article proposes a novel meta-learning-enabled …
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
H Su, L Xiang, A Hu, Y Xu, X Yang - Mechanical Systems and Signal …, 2022 - Elsevier
Recently, intelligent fault diagnosis has made great achievements, which has aroused
growing interests in the field of bearing fault diagnosis due to its strong feature learning …
growing interests in the field of bearing fault diagnosis due to its strong feature learning …
Transformer-based meta learning method for bearing fault identification under multiple small sample conditions
X Li, H Su, L Xiang, Q Yao, A Hu - Mechanical Systems and Signal …, 2024 - Elsevier
Most fault identification methods based on deep learning rely on a large amount of data, and
their effects are limited in the actual production environment. In the case of multiple …
their effects are limited in the actual production environment. In the case of multiple …