A review on guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques
The development of structural health monitoring (SHM) techniques is of great importance to
improve the structural efficiency and safety. With advantages of long propagation distances …
improve the structural efficiency and safety. With advantages of long propagation distances …
State-of-the-art review on advancements of data mining in structural health monitoring
To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …
statistical methods have been utilized in a remarkable number of structural health monitoring …
Crop type classification by DESIS hyperspectral imagery and machine learning algorithms
N Farmonov, K Amankulova, J Szatmári… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Developments in space-based hyperspectral sensors, advanced remote sensing, and
machine learning can help crop yield measurement, modelling, prediction, and crop …
machine learning can help crop yield measurement, modelling, prediction, and crop …
A transfer learning approach for acoustic emission zonal localization on steel plate-like structure using numerical simulation and unsupervised domain adaptation
The detection and localization of damage in metallic structures using acoustic emission (AE)
monitoring and artificial intelligence technology such as deep learning has been widely …
monitoring and artificial intelligence technology such as deep learning has been widely …
Machine-learning-based methods for acoustic emission testing: a review
G Ciaburro, G Iannace - Applied Sciences, 2022 - mdpi.com
Acoustic emission is a nondestructive control technique as it does not involve any input of
energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously …
energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously …
[HTML][HTML] A review of ultrasonic sensing and machine learning methods to monitor industrial processes
Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …
sensor measurements owing to their strong performance. These techniques also offer …
[HTML][HTML] Acoustic emission source location method and experimental verification for structures containing unknown empty areas
Acoustic emission (AE) localization plays an important role in the prediction and control of
potential hazardous sources in complex structures. However, existing location methods …
potential hazardous sources in complex structures. However, existing location methods …
Unsupervised learning framework for temperature compensated damage identification and localization in ultrasonic guided wave SHM with transfer learning
Damage localization algorithms for ultrasonic guided wave-based structural health
monitoring (GW-SHM) typically utilize manually-defined features and supervised machine …
monitoring (GW-SHM) typically utilize manually-defined features and supervised machine …
A hierarchical deep convolutional regression framework with sensor network fail-safe adaptation for acoustic-emission-based structural health monitoring
Lamb wave-based signals from sparse-distributed sensors are complicated and difficult to
process for structural health monitoring (SHM), not only due to their dispersive and multi …
process for structural health monitoring (SHM), not only due to their dispersive and multi …
Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach
H Mahajan, S Banerjee - Measurement Science and Technology, 2023 - iopscience.iop.org
An acoustic emission (AE) approach for non-destructive evaluation of structures has been
developed over the last two decades. In complex structures, one of the limitations of AE …
developed over the last two decades. In complex structures, one of the limitations of AE …