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A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
[HTML][HTML] A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges …
Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …
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 …
Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework
As one of the representative unsupervised data augmentation methods, generative
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …
Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis
Convolutional neural networks (CNNs) are being utilized for mechanical fault diagnosis, due
to its excellent automatic discriminative feature learning ability. However, the poor …
to its excellent automatic discriminative feature learning ability. However, the poor …
Fault diagnosis in rotating machines based on transfer learning: Literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
significant attention in recent years. However, traditional data-driven diagnosis approaches …
Universal source-free domain adaptation method for cross-domain fault diagnosis of machines
Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge
from a labeled source domain to a new unlabeled target domain. Most existing methods …
from a labeled source domain to a new unlabeled target domain. Most existing methods …
Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds
The existing deep transfer learning-based intelligent fault diagnosis studies for machinery
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …
Applications of machine learning to machine fault diagnosis: A review and roadmap
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …
machine fault diagnosis. This is a promising way to release the contribution from human …
GTFE-Net: A gramian time frequency enhancement CNN for bearing fault diagnosis
Fault diagnosis of the bearing is vital for the safe and reliable operation of rotating machines
in the manufacturing industry. Convolutional neural networks (CNNs) have been popular in …
in the manufacturing industry. Convolutional neural networks (CNNs) have been popular in …