Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
A comprehensive review on convolutional neural network in machine fault diagnosis
With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …
increasingly significant to ensure safe equipment operation and production. Consequently …
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 …
Fault diagnosis of rotating machinery based on recurrent neural networks
Fault diagnosis of rotating machinery is essential for maintaining system performance and
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …
A systematic review of data-driven approaches to fault diagnosis and early warning
As an important stage of life cycle management, machinery PHM (prognostics and health
management), an emerging subject in mechanical engineering, has seen a huge amount of …
management), an emerging subject in mechanical engineering, has seen a huge amount of …
SuperGraph: Spatial-temporal graph-based feature extraction for rotating machinery diagnosis
Vibration signals always contain noise and irregularities, which makes spectrum analysis
difficult to extract high-level features. Recently, graph theory has been applied to spectrum …
difficult to extract high-level features. Recently, graph theory has been applied to spectrum …
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 …
Dual-path mixed-domain residual threshold networks for bearing fault diagnosis
Y Chen, D Zhang, H Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Intelligent bearing fault diagnosis based on deep learning is one of the hotspots in
mechanical equipment monitoring applications. However, traditional deep learning-based …
mechanical equipment monitoring applications. However, traditional deep learning-based …
Coordinated approach fusing time-shift multiscale dispersion entropy and vibrational Harris hawks optimization-based SVM for fault diagnosis of rolling bearing
K Shao, W Fu, J Tan, K Wang - Measurement, 2021 - Elsevier
To fully mine the effective fault information and improve the fault diagnosis accuracy, a novel
fault diagnosis approach for rolling bearings is proposed by integrating variational mode …
fault diagnosis approach for rolling bearings is proposed by integrating variational mode …
CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis
X Yan, D Jiang, L Xiang, Y Xu, Y Wang - Information Fusion, 2024 - Elsevier
When the machinery device operates abnormally, it is not sufficient for fault detection only
via extracting fault features from a single sensor due to the latent fault information may be …
via extracting fault features from a single sensor due to the latent fault information may be …