The Acoustic Emission (AE) technique plays a progressively significant role in the field of non-destructive testing (NDT) especially in structural health monitoring (SHM). Acoustic emissions are commonly defined as transient elastic waves in a material caused by the of localized stress release. In using AE for structural diagnostics, noise has always been a potential barrier. AE can be produced from sources not related to material damage including traffic or friction. The major challenge is the differentiation of signals relevant to the purpose of the monitoring - such as crack growth in a member - from noise of various origins. This paper deals with noise discrimination and introduces a novel approach for noise interpretation in AE data. AE activities recorded in field and lab environments for concrete and steel specimens are investigated in this study. Approaches for clustering and separation of AE signals based on multiple features extracted from experimental data are presented.
Acoustic emission noise assessment of loaded members in laboratory and field environments / T., Shokri; N., Nemati; Nanni, Antonio. - 8347:(2012), pp. 83471T-1-83471T-8. (Intervento presentato al convegno Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security tenutosi a San Diego (USA) nel 11-15 March 2012) [10.1117/12.915327].
Acoustic emission noise assessment of loaded members in laboratory and field environments
NANNI, ANTONIO
2012
Abstract
The Acoustic Emission (AE) technique plays a progressively significant role in the field of non-destructive testing (NDT) especially in structural health monitoring (SHM). Acoustic emissions are commonly defined as transient elastic waves in a material caused by the of localized stress release. In using AE for structural diagnostics, noise has always been a potential barrier. AE can be produced from sources not related to material damage including traffic or friction. The major challenge is the differentiation of signals relevant to the purpose of the monitoring - such as crack growth in a member - from noise of various origins. This paper deals with noise discrimination and introduces a novel approach for noise interpretation in AE data. AE activities recorded in field and lab environments for concrete and steel specimens are investigated in this study. Approaches for clustering and separation of AE signals based on multiple features extracted from experimental data are presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
