Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network
This paper introduces a novel approach to fault detection in the servo motor bearings of
industrial robots within the context of Industry 4.0 prognostics and health management. The …
industrial robots within the context of Industry 4.0 prognostics and health management. The …
[HTML][HTML] A Review on Traditional and Artificial Intelligence-Based Preservation Techniques for Oil Painting Artworks
Oil paintings represent significant cultural heritage, as they embody human creativity and
historical narratives. The preservation of these invaluable artifacts requires effective …
historical narratives. The preservation of these invaluable artifacts requires effective …
[HTML][HTML] Delamination detection in CFRP laminates using deep transfer learning with limited experimental data
Carbon fiber reinforced polymer (CFRP) composites have been continuously replacing
conventional metallic materials due to their excellent material properties. The orthotropic …
conventional metallic materials due to their excellent material properties. The orthotropic …
Graph neural networks with trainable adjacency matrices for fault diagnosis on multivariate sensor data
Timely detection and accurate diagnosis of faults in technological processes can
significantly reduce production costs in manufacturing. Modern industrial equipment …
significantly reduce production costs in manufacturing. Modern industrial equipment …
Imbalanced class incremental learning system: A task incremental diagnosis method for imbalanced industrial streaming data
In recent years, machine learning has been widely used in various fault diagnosis scenarios.
However, existing machine learning algorithms tend to work well in closed static …
However, existing machine learning algorithms tend to work well in closed static …
A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis
X Zhang, C Li, C Han, S Li, Y Feng, H Wang… - Advanced Engineering …, 2024 - Elsevier
Intelligent fault diagnosis methods have made significant progress during the past decade,
often following a centralized training paradigm that gathers data from multiple industrial …
often following a centralized training paradigm that gathers data from multiple industrial …
Noise robust damage detection of laminated composites using multichannel wavelet-enhanced deep learning model
MM Azad, HS Kim - Engineering Structures, 2025 - Elsevier
This paper presents a noise-robust damage detection framework for composite structures
via a commonly used vibration-based non-destructive testing (NDT) method. Recently, deep …
via a commonly used vibration-based non-destructive testing (NDT) method. Recently, deep …
Deep Learning-Based Fracture Mode Determination in Composite Laminates
This study focuses on the determination of the fracture mode in composite laminates using
deep learning. With the increase in the use of laminated composites in numerous …
deep learning. With the increase in the use of laminated composites in numerous …
[HTML][HTML] Autonomous data-driven delamination detection in laminated composites with limited and imbalanced data
MM Azad, S Kim, HS Kim - Alexandria Engineering Journal, 2024 - Elsevier
This study addresses the challenges of data scarcity and class imbalance in structural health
monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from …
monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from …
Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal
Wind Turbines are the most crucial devices in wind energy conversion systems to increase
energy generation efficiency from wind sources. Blade failure in wind turbines is due to the …
energy generation efficiency from wind sources. Blade failure in wind turbines is due to the …