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 …
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Z Zhu, Y Lei, G Qi, Y Chai, N Mazur, Y An, X Huang - Measurement, 2023 - Elsevier
With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework
As one of the representative unsupervised data augmentation methods, generative
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …
Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review
A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
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 …
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 …
Universal source-free domain adaptation method for cross-domain fault diagnosis of machines
Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge
from a labeled source domain to a new unlabeled target domain. Most existing methods …
from a labeled source domain to a new unlabeled target domain. Most existing methods …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
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 …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …