Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials

Structural health monitoring (SHM) in fiber-reinforced polymer (FRP) composites is essential to ensure safety and reliability during service, particularly in critical industries such as aerospace and wind energy. Traditional methods of analyzing Acoustic Emission (AE) signals in the time domain ofte...

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Principais autores:	Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson, Mayorkinos Papaelias
格式:	Artigo
語言:	Inglês
出版:	MDPI AG 2025-03-01
叢編:	Infrastructures
主題:	structural health monitoring acoustic emission fast Fourier transform autoencoders deep learning clustering
在線閱讀:	https://www.mdpi.com/2412-3811/10/3/51
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