[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
A review on models to prevent and control lithium-ion battery failures: From diagnostic and prognostic modeling to systematic risk analysis
Q Yang, C Xu, M Geng, H Meng - Journal of Energy Storage, 2023 - Elsevier
The lithium-ion batteries (LIBs) are indispensible to fulfill the increasing demand for energy
storage. Simultaneously, accidents related to battery-powered facilities have been reported …
storage. Simultaneously, accidents related to battery-powered facilities have been reported …
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer
To enable researchers to fully trust the decisions made by deep diagnostic models,
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …
Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis
Transformer has been widely applied in the research of rotating machinery fault diagnosis
due to its ability to explore the internal correlation of vibration signals. However, challenges …
due to its ability to explore the internal correlation of vibration signals. However, challenges …
Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis
Due to the limitations of data quality and quantity of a single industrial user, the development
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …
An uncertainty perception metric network for machinery fault diagnosis under limited noisy source domain and scarce noisy unknown domain
Deep learning has made notable advances in intelligent fault diagnosis. However, industrial
application of deep learning models faces challenges due to noise interference and scarce …
application of deep learning models faces challenges due to noise interference and scarce …
Dynamic vision-based machinery fault diagnosis with cross-modality feature alignment
Intelligent machinery fault diagnosis methods have been popularly and successfully
developed in the past decades, and the vibration acceleration data collected by contact …
developed in the past decades, and the vibration acceleration data collected by contact …
Semisupervised subdomain adaptation graph convolutional network for fault transfer diagnosis of rotating machinery under time-varying speeds
P Liang, L Xu, H Shuai, X Yuan… - IEEE/ASME …, 2023 - ieeexplore.ieee.org
The deep learning-based fault diagnosis approaches have shown great advantages in
ensuring rotating machinery (RM) work normally and safely. However, in real industrial …
ensuring rotating machinery (RM) work normally and safely. However, in real industrial …
A novel domain generalization network with multidomain specific auxiliary classifiers for machinery fault diagnosis under unseen working conditions
The domain adaptation-based intelligent diagnosis approaches have achieved promising
performance on diagnosis tasks under different working conditions. However, these …
performance on diagnosis tasks under different working conditions. However, these …
An adaptive domain adaptation method for rolling bearings' fault diagnosis fusing deep convolution and self-attention networks
Intelligent fault diagnosis methods based on deep learning have attracted significant
attention in recent years. However, it still faces many challenges, including complex and …
attention in recent years. However, it still faces many challenges, including complex and …