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 …
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 …
Adaptive knowledge transfer by continual weighted updating of filter kernels for few-shot fault diagnosis of machines
Deep learning (DL) based diagnosis models have to be trained by large quantities of
monitoring data of machines. However, in real-case scenarios, machines operate under the …
monitoring data of machines. However, in real-case scenarios, machines operate under the …
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 …
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 …
A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions
T Hu, T Tang, R Lin, M Chen, S Han, J Wu - Measurement, 2020 - Elsevier
In the era of big data, various data-driven fault diagnosis algorithms, which are mainly based
on traditional machine learning and deep learning, have been developed and successfully …
on traditional machine learning and deep learning, have been developed and successfully …
Domain discrepancy-guided contrastive feature learning for few-shot industrial fault diagnosis under variable working conditions
Recent advances in data-driven methods have significantly promoted intelligent fault
diagnostics for varied industrial applications. However, due to the limitations of machine fault …
diagnostics for varied industrial applications. However, due to the limitations of machine fault …
A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis
Z Ren, Y Zhu, K Yan, K Chen, W Kang, Y Yue… - Mechanical systems and …, 2020 - Elsevier
Both of traditional intelligent fault diagnosis (TIFD) based on artificial features and modern
intelligent fault diagnosis (MIFD) based on deep learning have made healthy progress in …
intelligent fault diagnosis (MIFD) based on deep learning have made healthy progress in …
Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …
years; however, most of these methods are coarse grained and data demanding that cannot …