Attention gate guided multiscale recursive fusion strategy for deep neural network-based fault diagnosis
Rolling bearings are crucial for ensuring the safe and stable operation of electromechanical
systems. Although deep learning has been widely used in fault diagnosis of rolling bearings …
systems. Although deep learning has been widely used in fault diagnosis of rolling bearings …
A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data
Intelligent fault diagnosis with imbalanced data is a problem that often raises concerns. The
diagnosis is more effective when the imbalanced dataset is supplemented with data …
diagnosis is more effective when the imbalanced dataset is supplemented with data …
Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy
Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …
years, and promising diagnostic performance has been achieved with sufficient monitoring …
Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods
has shown significant efficacy in industrial settings, facilitating the transfer of operational …
has shown significant efficacy in industrial settings, facilitating the transfer of operational …
Digital twin-assisted fault diagnosis framework for rolling bearings under imbalanced data
The application of deep learning-based fault diagnosis methods is constrained by the
imbalanced data. Recently, many studies have suggested integrating dynamic model …
imbalanced data. Recently, many studies have suggested integrating dynamic model …
Digital twin-enabled entropy regularized wavelet attention domain adaptation network for gearboxes fault diagnosis without fault data
P Zhu, L Deng, B Tang, Q Yang, Q Li - Advanced Engineering Informatics, 2025 - Elsevier
Intelligent fault diagnosis methods based on domain adaptation play a vital role in ensuring
the long-term safe and reliable operation of gearboxes. However, the scarcity of high-quality …
the long-term safe and reliable operation of gearboxes. However, the scarcity of high-quality …
An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions
J Liu, H Cao, Y Luo - Reliability Engineering & System Safety, 2023 - Elsevier
Data-driven intelligent methods have achieved notable performance in the field of bearing
fault diagnosis under stationary conditions. However, in some actual scenarios such as high …
fault diagnosis under stationary conditions. However, in some actual scenarios such as high …
Twin data multimode collaborative transfer learning for bearing failure diagnosis
X Liu, Y **, F Yang, Y Kang, L Bo - Engineering Applications of Artificial …, 2024 - Elsevier
To tackle the challenges of gathering and labeling data in practical engineering
applications, a multimode collaborative transfer learning method is proposed to bridge the …
applications, a multimode collaborative transfer learning method is proposed to bridge the …
Multi-weight adversarial open-set domain adaptation network for machinery fault diagnosis with unknown faults
Domain adaptation (DA) methods have proven successful in addressing the domain-shift
challenge in rotating machinery fault diagnosis, and the basic tasks that the fault categories …
challenge in rotating machinery fault diagnosis, and the basic tasks that the fault categories …
Simulation data-driven adaptive frequency filtering focal network for rolling bearing fault diagnosis
The scarcity of well-labeled samples severely limits the application of deep learning-based
fault diagnosis methods. To address this issue, this paper proposes a novel domain …
fault diagnosis methods. To address this issue, this paper proposes a novel domain …