Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic
H Tao, L Cheng, J Qiu… - Measurement Science and …, 2022 - iopscience.iop.org
With the rapid development of industrial informatization and deep learning technology,
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …
A novel cross-domain fault diagnosis method based on model agnostic meta-learning
In real industrial scenarios, the working conditions of mechanical equipment are always
highly variable and the amount of data that can be collected is limited, which renders a …
highly variable and the amount of data that can be collected is limited, which renders a …
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 …
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 deep metric learning model for imbalanced fault diagnosis and toward open-set classification
C Wang, C Xin, Z Xu - Knowledge-Based Systems, 2021 - Elsevier
Intelligent fault diagnosis based on deep neural networks and big data has been an
attractive field and shows great prospects for applications. However, applications in practice …
attractive field and shows great prospects for applications. However, applications in practice …
A fault diagnosis method using improved prototypical network and weighting similarity-Manhattan distance with insufficient noisy data
C Wang, J Yang, B Zhang - Measurement, 2024 - Elsevier
Currently, few samples and the inevitable noise poses a severe test on deep learning
methods. To solve the above problems, a novel fault diagnosis network based on a refined …
methods. To solve the above problems, a novel fault diagnosis network based on a refined …
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 novel Brownian correlation metric prototypical network for rotating machinery fault diagnosis with few and zero shot learners
J Yang, C Wang - Advanced Engineering Informatics, 2022 - Elsevier
Due to the variability of working conditions and the scarcity of fault samples, the existing
diagnosis models still have a big gap under the condition of covering more practical …
diagnosis models still have a big gap under the condition of covering more practical …
Cross-domain meta learning fault diagnosis based on multi-scale dilated convolution and adaptive relation module
R Ma, T Han, W Lei - Knowledge-Based Systems, 2023 - Elsevier
For the established fault classification system, new faults cannot be identified due to lack of
training data in the process of equipment operation. Aiming at the problems of multi …
training data in the process of equipment operation. Aiming at the problems of multi …
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 …