Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application
Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …
recent years and has become an important technique for scholars to study and apply. The …
A CNN-BiLSTM model with attention mechanism for earthquake prediction
Earthquakes, as natural phenomena, have consistently caused damage and loss of human
life throughout history. Earthquake prediction is an essential aspect of any society's plans …
life throughout history. Earthquake prediction is an essential aspect of any society's plans …
Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis
Unsupervised domain adaptation (UDA) has shown remarkable results in fault diagnosis
under changing working conditions in recent years. However, most UDA methods do not …
under changing working conditions in recent years. However, most UDA methods do not …
A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions
Bearing fault diagnosis in real-world applications has challenges such as insufficient
labeled data, changing working conditions of the rotary machinery, and missing data due to …
labeled data, changing working conditions of the rotary machinery, and missing data due to …
[HTML][HTML] On the effects of data normalization for domain adaptation on EEG data
Abstract In Machine Learning (ML), a well-known problem is the Dataset Shift problem
where the data in the training and test sets can follow different probability distributions …
where the data in the training and test sets can follow different probability distributions …
A new unsupervised health index estimation method for bearings early fault detection based on Gaussian mixture model
Bearings are indispensable components of machinery, playing a critical role in effective
health monitoring. This monitoring is vital in detecting equipment incipient failure and …
health monitoring. This monitoring is vital in detecting equipment incipient failure and …
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
A Saeed, M A. Khan, U Akram, W J. Obidallah… - Scientific Reports, 2025 - nature.com
Industry 4.0 represents the fourth industrial revolution, which is characterized by the
incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big …
incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big …
Deep multi-scale dilated convolution neural network with attention mechanism: a novel method for earthquake magnitude classification
P Kavianpour, M Kavianpour… - 2022 8th Iranian …, 2022 - ieeexplore.ieee.org
The magnitude of an earthquake influences the amount of damage and casualties it causes
in any society. Although reliable and timely prediction of magnitude earthquakes can help …
in any society. Although reliable and timely prediction of magnitude earthquakes can help …
An interpretable hybrid framework combining convolution latent vectors with transformer based attention mechanism for rolling element fault detection and …
A Saeed, MU Akram, M Khattak, MB Khan - Heliyon, 2024 - cell.com
Failure of industrial assets can cause financial, operational and safety hazards across
different industries. Monitoring their condition is crucial for successful and smooth …
different industries. Monitoring their condition is crucial for successful and smooth …
Physics-Informed Deep Learning and Partial Transfer Learning for Bearing Fault Diagnosis in the Presence of Highly Missing Data
One of the most significant obstacles in bearing fault diagnosis is a lack of labeled data for
various fault types. Also, sensor-acquired data frequently lack labels and have a large …
various fault types. Also, sensor-acquired data frequently lack labels and have a large …