Coarse-to-fine: Progressive knowledge transfer-based multitask convolutional neural network for intelligent large-scale fault diagnosis
In modern industry, large-scale fault diagnosis of complex systems is emerging and
becoming increasingly important. Most deep learning-based methods perform well on small …
becoming increasingly important. Most deep learning-based methods perform well on small …
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 …
Domain knowledge-based deep-broad learning framework for fault diagnosis
J Feng, Y Yao, S Lu, Y Liu - IEEE Transactions on Industrial …, 2020 - ieeexplore.ieee.org
Intelligent fault diagnosis is a vital role in smart manufacturing. And deep-learning-based
fault diagnosis has become a hot topic due to its strong feature extraction ability. However …
fault diagnosis has become a hot topic due to its strong feature extraction ability. However …
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
A generalized graph contrastive learning framework for few-shot machine fault diagnosis
Graph data-driven machine fault diagnosis methods make success using sufficient data
recently. However, in the actual industry, there are rare failure data in historical data, leading …
recently. However, in the actual industry, there are rare failure data in historical data, leading …
Meta-learning based domain generalization framework for fault diagnosis with gradient aligning and semantic matching
Intelligent fault diagnosis models have de-monstrated superior performance in industrial
prognostics health management scenarios. However, these models may struggle to …
prognostics health management scenarios. However, these models may struggle to …
Domain adaptation meta-learning network with discard-supplement module for few-shot cross-domain rotating machinery fault diagnosis
Y Zhang, D Han, J Tian, P Shi - Knowledge-Based Systems, 2023 - Elsevier
Intelligent diagnostic methods based on deep learning have proven to be effective in
equipment management and maintenance. However, in practical industrial applications in …
equipment management and maintenance. However, in practical industrial applications in …
Few-shot one-class classification via meta-learning
Although few-shot learning and one-class classification (OCC), ie, learning a binary
classifier with data from only one class, have been separately well studied, their intersection …
classifier with data from only one class, have been separately well studied, their intersection …
Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge
C Qian, J Zhu, Y Shen, Q Jiang, Q Zhang - Neural Processing Letters, 2022 - Springer
Mechanical intelligent fault diagnosis is an important method to accurately identify the health
status of mechanical equipment and ensure its safe operation. With the advent of the “big …
status of mechanical equipment and ensure its safe operation. With the advent of the “big …
TScatNet: An interpretable cross-domain intelligent diagnosis model with antinoise and few-shot learning capability
C Liu, C Qin, X Shi, Z Wang, G Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In a real industrial scenario, domain shift frequently occurred due to working loads variation,
operation speeds variation, and environmental noise interference, severely degrading …
operation speeds variation, and environmental noise interference, severely degrading …