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.
Domaines
Sciences de l'ingénieur [physics]
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