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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Veröffentlicht in:	Composite structures Jg. 291; S. 115579
Hauptverfasser:	Rautela, Mahindra, Senthilnath, J., Monaco, Ernesto, Gopalakrishnan, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2022
Schlagworte:	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
Online-Zugang:	Volltext
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