Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Malatesta, Michelangelo Maria (2022) Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Monitoraggio e gestione delle strutture e dell'ambiente - sehm2, 34 Ciclo. DOI 10.48676/unibo/amsdottorato/10332.
Documenti full-text disponibili:
[img] Documento PDF (English) - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Creative Commons Attribution Non-commercial No Derivatives 4.0 (CC BY-NC-ND 4.0) .
Download (28MB)


Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Tipologia del documento
Tesi di dottorato
Malatesta, Michelangelo Maria
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
Structural Health Monitoring, Non Destructive Testing, Guided Waves, Lamb Waves, Heterogeneous Sensor Networks, dispersion curves, damage localization, beamforming, SLDV
Data di discussione
14 Giugno 2022

Altri metadati

Statistica sui download

Gestione del documento: Visualizza la tesi