Unsupervised Fouling Reconstruction in the Pipe Bend

Denys Iablonskyi, Carlos-Omar Rasgado-Moreno, Madis Ratassepp, Arto Klami, Edward Haeggström, Ari Salmi

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals.
Originalspråkengelska
Titel på värdpublikation2023 International Ultrasonics Symposium
Antal sidor3
FörlagIEEE
Utgivningsdatum7 nov. 2023
ISBN (tryckt)979-8-3503-4646-6
ISBN (elektroniskt)979-8-3503-4645-9
DOI
StatusPublicerad - 7 nov. 2023
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang2023 IEEE International Ultrasonics Symposium (IUS) - Palais des congrès de Montréal, Montréal, Kanada
Varaktighet: 3 sep. 20238 sep. 2023

Publikationsserier

Namn
ISSN (tryckt)1948-5719
ISSN (elektroniskt)1948-5727

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