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 …
A review of data-driven machinery fault diagnosis using machine learning algorithms
J Cen, Z Yang, X Liu, J Xiong, H Chen - Journal of Vibration Engineering & …, 2022 - Springer
Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …
driven machinery fault diagnosis based on machine learning algorithms, and provide …
Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method
J Li, Y Liu, Q Li - Measurement, 2022 - Elsevier
Data-driven intelligent method has been widely used in fault diagnostics. However, it is
observed that previous research studies focusing on imbalanced datasets for fault diagnosis …
observed that previous research studies focusing on imbalanced datasets for fault diagnosis …
CrackW-Net: A novel pavement crack image segmentation convolutional neural network
Image-based intelligent detection of road cracks with high accuracy and efficiency is vital to
the overall condition assessment of the pavement. However, significant problems of …
the overall condition assessment of the pavement. However, significant problems of …
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 …
Landslide detection mapping employing CNN, ResNet, and DenseNet in the three gorges reservoir, China
Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention;
however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze …
however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze …
Transfer learning with time series data: a systematic mapping study
Transfer Learning is a well-studied concept in machine learning, that relaxes the assumption
that training and testing data need to be drawn from the same distribution. Recent success in …
that training and testing data need to be drawn from the same distribution. Recent success in …
Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment …
Intelligent fault diagnosis methods are significant to mitigate the dependency on expert
knowledge and the cost. For the limited faulty data and variational working conditions of real …
knowledge and the cost. For the limited faulty data and variational working conditions of real …
Mapping post-earthquake landslide susceptibility using U-Net, VGG-16, VGG-19, and metaheuristic algorithms
Landslides are among the most frequent secondary disasters caused by earthquakes in
areas prone to seismic activity. Given the necessity of assessing the current seismic …
areas prone to seismic activity. Given the necessity of assessing the current seismic …
Pareto optimized adaptive learning with transposed convolution for image fusion Alzheimer's disease classification
Alzheimer's disease (AD) is a neurological condition that gradually weakens the brain and
impairs cognition and memory. Multimodal imaging techniques have become increasingly …
impairs cognition and memory. Multimodal imaging techniques have become increasingly …