Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
[HTML][HTML] Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
JM Ramírez-Sanz, JA Maestro-Prieto… - ISA transactions, 2023 - Elsevier
Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …
industries today. A myriad of methods are in use, although the most recent leading …
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 …
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …
popularly developed in the past years. While promising diagnostic performance has been …
Intelligent machinery fault diagnosis with event-based camera
Event-based cameras are the emerging bioinspired technology in vision sensing. Different
from the traditional standard cameras, the event-based cameras asynchronously record the …
from the traditional standard cameras, the event-based cameras asynchronously record the …
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
Data-driven approaches for prognostic and health management (PHM) increasingly rely on
massive historical data, yet annotations are expensive and time-consuming. Learning …
massive historical data, yet annotations are expensive and time-consuming. Learning …
Conditional GAN and 2-D CNN for bearing fault diagnosis with small samples
J Yang, J Liu, J Xie, C Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The rolling bearing is the key component of rotating machinery, and it is also a failure–prone
component. The intelligent fault diagnosis method has been widely used to accurately …
component. The intelligent fault diagnosis method has been widely used to accurately …
Transfer learning based on improved stacked autoencoder for bearing fault diagnosis
Deep transfer learning algorithm is regarded as a promising method to address the issue of
rolling bearing fault diagnosis with limited labeled data. Stacked autoencoder (SAE) has …
rolling bearing fault diagnosis with limited labeled data. Stacked autoencoder (SAE) has …
Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy
Intelligent data-driven machinery fault diagnosis methods have been popularly developed in
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions
Intelligent data-driven system prognostic methods have been popularly developed in the
recent years. Despite the promising results, most approaches assume the training and …
recent years. Despite the promising results, most approaches assume the training and …