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 …
Augmentation-based discriminative meta-learning for cross-machine few-shot fault diagnosis
Deep learning methods have demonstrated promising performance in fault diagnosis tasks.
Although the scarcity of data in industrial scenarios limits the practical application of such …
Although the scarcity of data in industrial scenarios limits the practical application of such …
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 …
A meta-learning network with anti-interference for few-shot fault diagnosis
Z Zhao, R Zhao, X Wu, X Hu, R Che, X Zhang, Y Jiao - Neurocomputing, 2023 - Elsevier
Considering the changing working conditions of rotating machinery in operation, it is often
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …
Meta-learning with adaptive learning rates for few-shot fault diagnosis
L Chang, YH Lin - IEEE/ASME Transactions on Mechatronics, 2022 - ieeexplore.ieee.org
Deep learning-based methods have been developed and widely used for fault diagnosis,
which rely on the sufficient data. However, fault data are extremely limited in some real-case …
which rely on the sufficient data. However, fault data are extremely limited in some real-case …
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 …
Novel joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis
J Hu, W Li, A Wu, Z Tian - Knowledge-Based Systems, 2023 - Elsevier
Traditional deep learning fails to identify new faults when the number of faulty samples is
limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not …
limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not …
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 …
Few-shot transfer learning for intelligent fault diagnosis of machine
Rotating machinery intelligent diagnosis with large data has been researched
comprehensively, while there is still a gap between the existing diagnostic model and the …
comprehensively, while there is still a gap between the existing diagnostic model and the …
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 …