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.

Structural health monitoring of storage tanks

In this thesis, Ph.D candidate presents a compact sensor node (SN) designed for long-term and real-time acoustic emission (AE) monitoring of above ground storage tanks (ASTs). Each SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms for effective leakage detection, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. Alternatively, capability to plug three high frequency AE sensors to a SN for corrosion simulated phenomena detection is envisaged and demonstrated. Another innovative aspect that the Ph.D candidate presents in this work is an alternative mathematical model of corrosion location on the bottom of the AST. This approach implies considering the three-dimensional localization model versus the two-dimensional commonly used according to the literature. This approach is aimed at significant optimization in the number of sensors in relation to the standard approach for solving localization problems as well as to allow filtering the false AE events related to the condensate droplets from AST ceiling. The technological implementation of this concept required the solution of a number of technical problems, such as the precise time of arrival (ToA) signal estimation, vertical localization of the AE source and multilaration solution that were discussed in detail in this work. To validate the developed prototype, several experimental campaigns were organized that included the simulation of target phenomena both in laboratory conditions and on a real water storage tank. The presented test results demonstrate the successful application of the developed AE system both for simulated leaks and for corrosion processes on the tank bottom. Mathematical and technological algorithms for localization and characterization of AE signals implemented during the development of the prototype are also confirmed by the test results.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.