Deep learning algorithms for bearing fault diagnostics—A comprehensive review
In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …
Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review
Z Yang, B Xu, W Luo, F Chen - Measurement, 2022 - Elsevier
With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images
The bearings are the crucial components of rotating machines in an industrial firm.
Unplanned failure of these components not only increases the downtime, but also leads to …
Unplanned failure of these components not only increases the downtime, but also leads to …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
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 …
A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification
Convolutional neural network (CNN) has gained increasing attention in fault classification.
However, the performance of CNN is sensitive to its learning rate. Some previous works …
However, the performance of CNN is sensitive to its learning rate. Some previous works …
Vibration-based damage detection for bridges by deep convolutional denoising autoencoder
One of the main challenges for structural damage detection using monitoring data is to
acquire features that are sensitive to damages but insensitive to noise (eg sensor …
acquire features that are sensitive to damages but insensitive to noise (eg sensor …
Deep regularized variational autoencoder for intelligent fault diagnosis of rotor–bearing system within entire life-cycle process
X Yan, D She, Y Xu, M Jia - Knowledge-Based Systems, 2021 - Elsevier
The performance of complex rotor–bearing system usually decreases with the development
of the running time, which indicates that the rotor–bearing system usually goes through …
of the running time, which indicates that the rotor–bearing system usually goes through …
Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems
A challenging problem in risk and reliability analysis of Complex Engineering Systems
(CES) is performing and updating risk and reliability assessments on the whole system with …
(CES) is performing and updating risk and reliability assessments on the whole system with …
Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models
Many industries are evaluating the use of the Internet of Things (IoT) technology to perform
remote monitoring and predictive maintenance on their mission-critical assets and …
remote monitoring and predictive maintenance on their mission-critical assets and …