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

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

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.

• Language: English
• Keywords: polymer composites, acoustic emission, feature extraction, convolutional autoencoders, deep features
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 13 str.
• Numbering: Vol. 12, iss. 4, art. 1867
• PID: 20.500.12556/RUL-135012
• UDC: 620.179.17:678
• ISSN on article: 2076-3417
• DOI: 10.3390/app12041867
• COBISS.SI-ID: 97798403
• Publication date in RUL: 17.02.2022

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 11.02.2022

Secondary language

• Language: Slovenian
• Keywords: polimerni kompoziti, akustična emisija, konvolucijski avtoenkoderji, izpeljava značilk, globoke značilke

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0241
• Name: Sinergetika kompleksnih sistemov in procesov

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0270
• Name: Proizvodni sistemi, laserske tehnologije in spajanje materialov