Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations

With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled a...

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Vydáno v:	Composite structures Ročník 291; s. 115579
Hlavní autoři:	Rautela, Mahindra, Senthilnath, J., Monaco, Ernesto, Gopalakrishnan, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.07.2022
Témata:	Convolutional autoencoders (CAE) Delamination detection Independent component analysis (ICA) One-class support vector machines (ocSVM) Principal component analysis (PCA) Delamination detection Independent component analysis (ICA) One-class support vector machines (ocSVM) Principal component analysis (PCA) Convolutional autoencoders (CAE)
ISSN:	0263-8223, 1879-1085
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