Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
In the engineering practice, lacking of data especially labeled data typically hinders the wide
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions
In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous
development thanks to its powerful feature representation ability. However, scarcity of high …
development thanks to its powerful feature representation ability. However, scarcity of high …
Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
Few-shot learning for fault diagnosis with a dual graph neural network
H Wang, J Wang, Y Zhao, Q Liu, M Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent
manufacturing systems. Deep learning-based methods have been recently developed for …
manufacturing systems. Deep learning-based methods have been recently developed for …
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 …
Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data
Recently, deep learning-based intelligent fault diagnosis methods have been developed
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …
Semisupervised momentum prototype network for gearbox fault diagnosis under limited labeled samples
X Zhang, Z Su, X Hu, Y Han… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
It is difficult to obtain expensive labeled data in industrial fault diagnosis applications, which
easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this …
easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this …
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 …
Self-adaptation graph attention network via meta-learning for machinery fault diagnosis with few labeled data
Effective application of fault diagnosis models requires that new fault types can be
recognized rapidly after they occur few times, even only one time. To this end, a self …
recognized rapidly after they occur few times, even only one time. To this end, a self …
Transfer relation network for fault diagnosis of rotating machinery with small data
Many deep-learning methods have been developed for fault diagnosis. However, due to the
difficulty of collecting and labeling machine fault data, the datasets in some practical …
difficulty of collecting and labeling machine fault data, the datasets in some practical …