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 …
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 …
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 …
Subdomain adaptation transfer learning network for fault diagnosis of roller bearings
Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …
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 …
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 …
Machinery fault diagnosis based on domain adaptation to bridge the gap between simulation and measured signals
In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly
dependent on labeled training samples, which may not be obtained in real-world …
dependent on labeled training samples, which may not be obtained in real-world …
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 …