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 …
Fault diagnosis in rotating machines based on transfer learning: Literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
significant attention in recent years. However, traditional data-driven diagnosis approaches …
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 …
A survey of transfer learning for machinery diagnostics and prognostics
In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …
components greatly influence operational safety and system reliability. Many data-driven …
Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review
Abstract Machine vision based condition monitoring and fault diagnosis of machine tools
(MVCMFD-MTs) is a vital technique of condition-based maintenance (CBM) in both metal …
(MVCMFD-MTs) is a vital technique of condition-based maintenance (CBM) in both metal …
Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis
K Zhao, H Jiang, K Wang, Z Pei - Knowledge-Based Systems, 2021 - Elsevier
Numerous intelligent methods have been developed to approach the challenges of fault
diagnosis. However, due to the different distributions of training samples and test samples …
diagnosis. However, due to the different distributions of training samples and test samples …
Targeted transfer learning through distribution barycenter medium for intelligent fault diagnosis of machines with data decentralization
Deep transfer learning-based fault diagnosis of machines is achieved based on the
assumption that the source and target domain data could be centralized to assess the …
assumption that the source and target domain data could be centralized to assess the …
Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis
Intelligent fault diagnosis aims to build robust mechanical condition recognition models with
limited dataset. At this stage, fault diagnosis faces two practical challenges:(1) the variability …
limited dataset. At this stage, fault diagnosis faces two practical challenges:(1) the variability …
Domain adaptation meta-learning network with discard-supplement module for few-shot cross-domain rotating machinery fault diagnosis
Y Zhang, D Han, J Tian, P Shi - Knowledge-Based Systems, 2023 - Elsevier
Intelligent diagnostic methods based on deep learning have proven to be effective in
equipment management and maintenance. However, in practical industrial applications in …
equipment management and maintenance. However, in practical industrial applications in …
A multisource dense adaptation adversarial network for fault diagnosis of machinery
Deep learning theory has made great progress in the field of intelligent fault diagnosis, and
the development of domain adaptation has greatly promoted fault diagnosis under polytropic …
the development of domain adaptation has greatly promoted fault diagnosis under polytropic …