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 …
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 …
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 …
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 …
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 …
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 …
Meta-learning with elastic prototypical network for fault transfer diagnosis of bearings under unstable speeds
Existing studies on meta-learning based few-shot fault diagnosis largely focus on constant
speed scenarios, neglecting the consideration of more realistic scenarios involving unstable …
speed scenarios, neglecting the consideration of more realistic scenarios involving unstable …
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 …