A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
Deep transfer learning for bearing fault diagnosis: A systematic review since 2016
The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …
training and test data follow the same distribution. This assumption, however, is not always …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains
Transfer learning based on a single source domain to a target domain has received a lot of
attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical …
attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical …
[HTML][HTML] A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges …
M Hakim, AAB Omran, AN Ahmed, M Al-Waily… - Ain Shams Engineering …, 2023 - Elsevier
Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …
popularly developed in the past years. While promising diagnostic performance has been …
Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives
Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis
W Zhang, Z Wang, X Li - Reliability Engineering & System Safety, 2023 - Elsevier
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 …
Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives
Intelligent fault diagnosis has been a promising way for condition-based maintenance.
However, the small sample problem has limited the application of intelligent fault diagnosis …
However, the small sample problem has limited the application of intelligent fault diagnosis …
Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study
Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …
representation learning and plenty of labeled data. However, machines often operate with …