Application of an Unsupervised Pattern Recognition Approach for AE Data Originating from Fatigue Tests on CFRP - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Application of an Unsupervised Pattern Recognition Approach for AE Data Originating from Fatigue Tests on CFRP

Résumé

Acoustic Emission (AE) technique is gaining more and more interest for structural health monitoring (SHM) in polymer-composite materials. Recent literature has shown that using appropriate pattern recognition techniques (PRT), the identification of the natural clusters of acoustic emission data can be obtained. Despite these recent and valuable advances and to achieve health assessment of composite materials, the scientific community faces two major challenges: (i) develop real-time approaches and (ii) propose clustering approaches able to process in in-service-like situation, i.e. in case of high AE activity generated simultaneously from many damage sources in material, from damage progression and cumulated damage and from noise. This work investigates acoustic emission generated during tension fatigue tests carried out on a carbon fiber reinforced polymer (CFRP) composite specimen. The acoustic emission signals detected during testing were analyzed by feature based pattern recognition techniques. In previous studies, it was demonstrated that the presented approach for detection of AE sources related to noise or damage is suitable. In the present paper, AE data originating from different stress amplitudes of cyclic loading tests have been used to reveal the effectiveness and the capacity of generalization of the proposed methodology including noise removal, feature selection and automatic separation of AE events.
Fichier principal
Vignette du fichier
266e66fa-416c-4367-a88f-b26a0a57402f-editor.pdf (1.17 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-01145011 , version 1 (23-04-2015)

Identifiants

  • HAL Id : hal-01145011 , version 1

Citer

Dong Dinh, Emmanuel Ramasso, Vincent Placet, Lamine Boubakar, Noureddine Zerhouni. Application of an Unsupervised Pattern Recognition Approach for AE Data Originating from Fatigue Tests on CFRP. 31st Conference of the European Working Group on Acoustic Emission (EWGAE), Sep 2014, Dresde, Germany. ⟨hal-01145011⟩
190 Consultations
70 Téléchargements

Partager

Gmail Facebook X LinkedIn More