A framework for the damage evaluation of acoustic emission signals through Hilbert–Huang transform. (15th June 2016)

• Record Type: Journal Article
• Authors: Siracusano, Giulio; Lamonaca, Francesco; Tomasello, Riccardo; Garescì, Francesca; Corte, Aurelio La; Carnì, Domenico Luca; Carpentieri, Mario; Grimaldi, Domenico; Finocchio, Giovanni
• Is Part Of: Mechanical systems and signal processing. Volume 75(2016)
• Page Start: 109
• Page End: 122
• Publication Date: 2016-06-15
• Subjects: Acoustic emission -- Damage detection -- Structural Health Monitoring -- Compression test -- Hilbert–Huang Transform -- 3D crack localization; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.12.004
• Languages: English
• ISSNs: 0888-3270

Abstract

Ab s tract: The acoustic emission (AE) is a powerful and potential nondestructive testing method for structural monitoring in civil engineering. Here, we show how systematic investigation of crack phenomena based on AE data can be significantly improved by the use of advanced signal processing techniques. Such data are a fundamental source of information that can be used as the basis for evaluating the status of the material, thereby paving the way for a new frontier of innovation made by data-enabled analytics. In this article, we propose a framework based on the Hilbert–Huang Transform for the evaluation of material damages that (i) facilitates the systematic employment of both established and promising analysis criteria, and (ii) provides unsupervised tools to achieve an accurate classification of the fracture type, the discrimination between longitudinal ( P -) and traversal ( S -) waves related to an AE event. The experimental validation shows promising results for a reliable assessment of the health status through the monitoring of civil infrastructures. Graphical abstract: Highlights: A processing framework based on Hilbert–Huang Transform (HHT) for Acoustic Emission (AE) data is proposed. The use of HHT gives rise to a superior denoising properties. Reliable identification of P - and S -wave and robust AE parameters estimation. Multilateration (MLT) achieves accurate analysis of crack evolution and propagation speed estimation. The results show the potential application of the framework for Structural Health Monitoring.