Multi-sensor data fusion-enabled semi-supervised optimal temperature-guided PCL framework for machinery fault diagnosis
Due to the extremely limited prior knowledge, machinery fault diagnosis under varying
working conditions with limited annotation data is a very challenging task in practical …
working conditions with limited annotation data is a very challenging task in practical …
Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis
Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee
safety and reliability of mechanical systems. Due to the variability of machinery working …
safety and reliability of mechanical systems. Due to the variability of machinery working …
Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics
In the past years, deep learning-based machinery fault diagnosis methods have been
successfully developed, and the basic diagnostic problems have been well addressed …
successfully developed, and the basic diagnostic problems have been well addressed …
Deep learning-based cross-sensor domain adaptation for fault diagnosis of electro-mechanical actuators
Recently, the development of intelligent data-driven machinery fault diagnosis methods
have received significant attention. In most studies, the training and testing data are …
have received significant attention. In most studies, the training and testing data are …
Towards prediction constraints: A novel domain adaptation method for machine fault diagnosis
Domain adaptation technologies have been extensively explored and successfully applied
to machine fault diagnosis, aiming to address problems that target data are unlabeled and …
to machine fault diagnosis, aiming to address problems that target data are unlabeled and …
Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis
Data driven-based intelligent fault diagnosis methods, as a promising approach, have been
widely employed in the health management and maintenance decision of rotating …
widely employed in the health management and maintenance decision of rotating …
Dual-weight consistency-induced partial domain adaptation network for intelligent fault diagnosis of machinery
Domain adaptation (DA)-based methods have been broadly developed for cross-domain
fault diagnosis of machinery, in which the target and source label spaces are identical …
fault diagnosis of machinery, in which the target and source label spaces are identical …
A novel domain generalization network with multidomain specific auxiliary classifiers for machinery fault diagnosis under unseen working conditions
The domain adaptation-based intelligent diagnosis approaches have achieved promising
performance on diagnosis tasks under different working conditions. However, these …
performance on diagnosis tasks under different working conditions. However, these …
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 …
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 …