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 …
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Vibration measurement and monitoring are essential in a wide variety of applications.
Vibration measurements are critical for diagnosing industrial machinery malfunctions …
Vibration measurements are critical for diagnosing industrial machinery malfunctions …
Dual-threshold attention-guided GAN and limited infrared thermal images for rotating machinery fault diagnosis under speed fluctuation
End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited
samples is challenging in industrial practice. The existing limited samples methods usually …
samples is challenging in industrial practice. The existing limited samples methods usually …
FGDAE: A new machinery anomaly detection method towards complex operating conditions
Recent studies on machinery anomaly detection only based on normal data training models
have yielded good results in improving operation reliability. However, most of the studies …
have yielded good results in improving operation reliability. However, most of the studies …
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …
Management (PHM) in recent years, because of their powerful feature representation ability …
Toward cognitive predictive maintenance: A survey of graph-based approaches
Abstract Predictive Maintenance (PdM) has continually attracted interest from the
manufacturing community due to its significant potential in reducing unexpected machine …
manufacturing community due to its significant potential in reducing unexpected machine …
Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset
EF Swana, W Doorsamy, P Bokoro - Sensors, 2022 - mdpi.com
Data-driven methods have prominently featured in the progressive research and
development of modern condition monitoring systems for electrical machines. These …
development of modern condition monitoring systems for electrical machines. These …
Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
In the engineering practice, lacking of data especially labeled data typically hinders the wide
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
Normalized conditional variational auto-encoder with adaptive focal loss for imbalanced fault diagnosis of bearing-rotor system
The distribution of the health data monitored from mechanical system in the industries is
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …
Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing
Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …