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 …
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 …
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 …
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 …
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 …
Prior knowledge-augmented self-supervised feature learning for few-shot intelligent fault diagnosis of machines
T Zhang, J Chen, S He, Z Zhou - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Data-driven intelligent diagnosis models expect to mine the health information of machines
from massive monitoring data. However, the size of faulty monitoring data collected in …
from massive monitoring data. However, the size of faulty monitoring data collected in …
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 …
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 …
Industrial edge intelligence: Federated-meta learning framework for few-shot fault diagnosis
The scarcity of fault samples has been the bottleneck for the large-scale application of
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …