Given the growing amount of industrial data in the 4th industrial revolution, deep learning
solutions have become popular for predictive maintenance (PdM) tasks, which involve …

Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …

Acoustic emission (AE) localization algorithms based on homogeneous media or single-
velocity are less accurate when applied to the triaxial localization experiments. To the end, a …

An improved acoustic emission (AE) based structural damage source localization method for
wind turbine blade is proposed in this paper. Firstly, the dispersion relations of wind turbine …

The use of machine learning in structural health monitoring is becoming more common, as
many of the inherent tasks (such as regression and classification) in develo** condition …

Acoustic emission (AE) localization plays an important role in the prediction and control of
potential hazardous sources in complex structures. However, existing location methods …

Rock macrofracturing often involves the tension-shear coexistence mechanism on the
microscopic scale. This paper addresses the occurrence of tension and shear mechanisms …

A novel spatial–temporal graph convolutional networks (STGCN) based method for the
regression task of localizing acoustic emission (AE) sources in composite panels is …

Despite the growing availability of sensing and data in general, we remain unable to fully
characterize many in-service engineering systems and structures from a purely data-driven …

An acoustic emission (AE) and graph convolutional network (GCN) based single-sensor
method for structural damage localization in wind turbine blades is proposed in this paper …