Deep feature extraction based on AE signals for the characterization of loaded carbon fiber epoxy and glass fiber epoxy composites

Potočnik, Primož (avtor), Misson, Martin (avtor), Šturm, Roman (avtor), Govekar, Edvard (avtor), Kek, Tomaž (avtor)

Characterization of acoustic emission (AE) signals in loaded materials can reveal structural damage and consequently provide early warnings about product failures. Therefore, extraction of the most informative features from AE signals is an important part of the characterization process. This study considers the characterization of AE signals obtained from bending experiments for carbon fiber epoxy (CFE) and glass fiber epoxy (GFE) composites. The research is focused on the recognition of material structure (CFE or GFE) based on the analysis of AE signals. We propose the extraction of deep features using a convolutional autoencoder (CAE). The deep features are compared with extracted standard AE features. Then, the different feature sets are analyzed through decision trees and discriminant analysis, combined with feature selection, to estimate the predictive potential of various feature sets. Results show that the application of deep features increases recognition accuracy. By using only standard AE-based features, a classification accuracy of around 80% is obtained, and adding deep features improves the classification accuracy to above 90%. Consequently, the application of deep feature extraction is encouraged for the characterization of loaded CFE composites.

• Jezik: Angleški jezik
• Ključne besede: polymer composites, acoustic emission, feature extraction, convolutional autoencoders, deep features
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 13 str.
• Številčenje: Vol. 12, iss. 4, art. 1867
• PID: 20.500.12556/RUL-135012
• UDK: 620.179.17:678
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app12041867
• COBISS.SI-ID: 97798403
• Datum objave v RUL: 17.02.2022

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 11.02.2022

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: polimerni kompoziti, akustična emisija, konvolucijski avtoenkoderji, izpeljava značilk, globoke značilke

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0241
• Naslov: Sinergetika kompleksnih sistemov in procesov

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0270
• Naslov: Proizvodni sistemi, laserske tehnologije in spajanje materialov