Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications

Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

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.

Oceanic Near-inertial internal waves generation, propagation and interaction with mesoscale dynamics

Oceans play a key role in the climate system, being the largest heat sinks on Earth. Part of the energy balance of ocean circulation is driven by the Near-inertial internal waves (NIWs). Strong NIWs are observed during a multi-platform, multi-disciplinary and multi-scale campaign led by the NATO-STO CMRE in autumn 2017 in the Ligurian Sea (northwestern Mediterranean Sea). The objectives of this work are as follows: characterise the studied area at different scales; study the NIWs generation and their propagation; estimate the NIWs properties; study the interaction between NIWs and mesoscale structures. This work provides, to the author’s knowledge, the first characterization of NIWs in the Mediterranean Sea. The near-surface NIWs observed at the fixed moorings are locally generated by wind bursts while the deeper waves originate in other regions and arrive at the moorings several days later. Most of the observed NIWs energy propagates downward with a mean vertical group velocity of (2.2±0.3) ⋅10-4 m s-1. On average, the NIWs have an amplitude of 0.13 m s-1 and mean horizontal and vertical wavelengths of 43±25 km and 125±35 m, while shorter wavelengths are observed at the near-coastal mooring, 36±2 km and 33±2 m, respectively. Most of the observed NIWs are blue shifted and reach a value 9% higher than the local inertial frequency. Only two observed NIWs are characterised by a redshift (up to 3% lower than the local inertial frequency). In support of the in situ observations, a high resolution numerical model is implemented using NEMO (Madec et al., 2019). Results show that anticyclones (cyclones) shift the frequency of NIWs to lower (higher) frequencies with respect to the local inertial frequency. Anticyclones facilitate the downward propagation of NIW energy, while cyclones dampen it. Absence of NIWs energy within an anticyclone is also investigated.