A novel momentum prototypical neural network to cross-domain fault diagnosis for rotating machinery subject to cold-start
Cross-domain rotating machinery fault diagnosis has achieved great success recently with
the development of deep transfer learning. However, conventional deep transfer learning …
the development of deep transfer learning. However, conventional deep transfer learning …
Model-assisted multi-source fusion hypergraph convolutional neural networks for intelligent few-shot fault diagnosis to electro-hydrostatic actuator
Abstract Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system
widely used in aerospace equipment. To ensure its normal operation, the intelligent fault …
widely used in aerospace equipment. To ensure its normal operation, the intelligent fault …
Gradient flow-based meta generative adversarial network for data augmentation in fault diagnosis
To date, various meta-learning methods have been explored to face the data-scarcity
problem in fault diagnosis. Almost without exception, these methods work on the premise …
problem in fault diagnosis. Almost without exception, these methods work on the premise …
An intelligent method for early motor bearing fault diagnosis based on Wasserstein distance generative adversarial networks meta learning
P Luo, Z Yin, D Yuan, F Gao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The fault diagnosis method based on generative adversarial networks (GANs) has been
successfully applied to the early fault detection of motor bearings, and it has effectively …
successfully applied to the early fault detection of motor bearings, and it has effectively …
Lightweight convolutional transformers enhanced meta-learning for compound fault diagnosis of industrial robot
Recent advance of deep learning has seen remarkable progress in compound fault
diagnosis modeling for industrial robots. Nevertheless, the data scarcity of compound fault …
diagnosis modeling for industrial robots. Nevertheless, the data scarcity of compound fault …
Signal processing collaborated with deep learning: An interpretable FIRNet for industrial intelligent diagnosis
L Rui, X Ding, S Wu, Q Wu, Y Shao - Mechanical Systems and Signal …, 2024 - Elsevier
Due to the neglect of prior characteristics and the lack of explicit constraints on fault
knowledge, conventional intelligent diagnosis methods suffer from great hardships in …
knowledge, conventional intelligent diagnosis methods suffer from great hardships in …
Meta-learning with adaptive learning rates for few-shot fault diagnosis
L Chang, YH Lin - IEEE/ASME Transactions on Mechatronics, 2022 - ieeexplore.ieee.org
Deep learning-based methods have been developed and widely used for fault diagnosis,
which rely on the sufficient data. However, fault data are extremely limited in some real-case …
which rely on the sufficient data. However, fault data are extremely limited in some real-case …
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 …
Self-supervised metalearning generative adversarial network for few-shot fault diagnosis of hoisting system with limited data
Y Li, F Xu, CG Lee - IEEE Transactions on Industrial Informatics, 2022 - ieeexplore.ieee.org
Few-shot data collected from hoisting system suffer from inadequate information in the
practical industries, which reduces the diagnostic accuracy of the data-driven-based fault …
practical industries, which reduces the diagnostic accuracy of the data-driven-based fault …
A meta-learning network with anti-interference for few-shot fault diagnosis
Z Zhao, R Zhao, X Wu, X Hu, R Che, X Zhang, Y Jiao - Neurocomputing, 2023 - Elsevier
Considering the changing working conditions of rotating machinery in operation, it is often
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …