Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis
Q Li - Expert Systems with Applications, 2023 - Elsevier
Sparse regularization has been attracting much attention in industrial applications over the
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy
Transfer learning can effectively solve the target task identification problem with the
prerequisite of sharing all user data and target data, and has become one of the most …
prerequisite of sharing all user data and target data, and has become one of the most …
Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks
In recent years, intelligent data-driven prognostic methods have been successfully
developed, and good machinery health assessment performance has been achieved …
developed, and good machinery health assessment performance has been achieved …
A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis
L Wan, Y Li, K Chen, K Gong, C Li - Measurement, 2022 - Elsevier
The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …
Open-set domain adaptation in machinery fault diagnostics using instance-level weighted adversarial learning
Data-driven machinery fault diagnosis methods have been successfully developed in the
past decades. However, the cross-domain diagnostic problems have not been well …
past decades. However, the cross-domain diagnostic problems have not been well …
Applications of federated learning; Taxonomy, challenges, and research trends
The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …
learning and deep learning models for edge network optimization. Although a complex edge …
Class-imbalance privacy-preserving federated learning for decentralized fault diagnosis with biometric authentication
Privacy protection as a major concern of the industrial big data enabling entities makes the
massive safety-critical operation data of a wind turbine unable to exert its great value …
massive safety-critical operation data of a wind turbine unable to exert its great value …
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 …
Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy
Intelligent data-driven machinery fault diagnosis methods have been popularly developed in
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …