Class-aware adversarial multiwavelet convolutional neural network for cross-domain fault diagnosis
Incomplete feature extraction and underutilization of unlabeled target data exist in the actual
situation of rotating machinery fault diagnosis. To this end, a class-aware adversarial …
situation of rotating machinery fault diagnosis. To this end, a class-aware adversarial …
Adversarial deep transfer learning in fault diagnosis: progress, challenges, and future prospects
Y Guo, J Zhang, B Sun, Y Wang - Sensors, 2023 - mdpi.com
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …
superiorities of deep learning in feature representation with the merits of transfer learning in …
Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis
The mainstream approach to addressing the issues of insufficient historical data and high
annotation costs in the domain of rotating machinery is to build transfer learning models …
annotation costs in the domain of rotating machinery is to build transfer learning models …
Using deep learning architectures for detection and classification of diabetic retinopathy
Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the
human eye and potentially leading to permanent blindness. The early detection of DR is …
human eye and potentially leading to permanent blindness. The early detection of DR is …
Digital twin enabled domain adversarial graph networks for bearing fault diagnosis
The fault diagnosis of rolling bearings is of utmost importance in industrial applications to
ensure mechanical systems' reliability, safety, and economic viability. However …
ensure mechanical systems' reliability, safety, and economic viability. However …
Federated domain generalization: A survey
Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …
identical and that data is centrally stored for training and testing. However, in real-world …
Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery
The distribution of monitored data during the service life of machinery equipment is
imbalanced, especially there is more monitoring data for health conditions than for failure …
imbalanced, especially there is more monitoring data for health conditions than for failure …
Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines
In recent years, deep transfer learning techniques have been successfully applied to solve
RUL prediction across different working conditions. However, for RUL prediction across …
RUL prediction across different working conditions. However, for RUL prediction across …
Self-paced decentralized federated transfer framework for rotating machinery fault diagnosis with multiple domains
Leveraging distributed data from various clients to tackle target issues has become a
prominent trend in fault diagnosis. However, the growing concerns about data privacy have …
prominent trend in fault diagnosis. However, the growing concerns about data privacy have …
Intelligent fault diagnosis via ring-based decentralized federated transfer learning
Federated transfer learning (FTL) can effectively address the data silos and domain shift that
exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for …
exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for …