Master of Science

thesisCarbon fiber-reinforced composite materials have been increasingly used in aerospace and aeronautics industries due to their superior strength over metals, low fatigue life, high corrosion resistance, and temperature resistance. Since most damage, such as delaminations, manifest inside the composite material, we often cannot detect damage through visual inspection. As a replacement for visual inspection, ultrasonic guided waves have been widely researched to remotely detect, locate, and characterize damage in structures due to their unique capability to travel long distances and inspect inaccessible locations for damage. Yet the anisotropic nature of composites makes it difficult to identify the velocity characteristics of the guided waves and utilize them for damage localization. To address this challenge, we use sparse wavenumber analysis to determine anisotropic multimodal and dispersive frequency-wavenumber characteristics of guided waves. We then use these multimodal and dispersive properties to predict how guided waves propagate in the anisotropic plate through sparse wavenumber synthesis. Finally, these predictions, which form a wave propagation model for the composite, are integrated with matched field processing, a model-based localization framework, to locate damage on the composite

Multi-helical Lamb Wave Imaging for Pipe-like Structures Based on a Probabilistic Reconstruction Approach

The special form of pipe-like structure provides the helical route for ultrasonic guided wave. Considering the pipe as a flattened plate but with periodical replications, the helical wave becomes intuitional and a corresponding imaging algorithm can be constructed. This work proposes the multihelical Lamb wave imaging method by utilizing the multiple arrival wavepackets which are denoted as different orders. The helical wave signal model is presented and the constant group velocity point is illustrated. The probabilistic reconstruction algorithm is combined with the separation and fusion of different helical routes. To verify the proposed scheme, finite element simulations and corresponding experiments are conducted. The cases of single-defect simulation and two-defect simulation indicate the successful and robust implementation of the imaging algorithm. The test on actual pipe damage is also investigated to show its capability in imaging an irregular defect. The comparison with imaging results from only first arrival demonstrates the advantage of multihelical wave imaging, including the better imaging resolution and higher localization accuracy

Compressive Sensing and Imaging of Guided Ultrasonic Wavefields

Structural health monitoring (SHM) and Nondestructive Evaluation (NDE) technologies can be used to predict the structural remaining useful life through appropriate diagnosis and prognosis methodologies. The main goal is the detection and characterization of defects that may compromise the integrity and the operability of a structure. The use of Lamb waves, which are ultrasonic guided waves (GW), have shown potential for detecting damage in specimens as a part of SHM or NDT systems. These methods can play a significant role in monitoring and tracking the integrity of structures by estimating the presence, location, severity, and type of damage. One of the advantages of GW is their capacity to propagate over large areas with excellent sensitivity to a variety of damage types while guaranteeing a short wavelength, such that the detectability of large structural damages is guaranteed. The Guided ultrasonic wavefield imaging (GWI) is an advanced technique for Damage localization and identification on a structure. GWI is generally referred to as the analysis of a series of images representing the time evolution of propagating waves and, possibly, their interaction with defects. This technique can provide useful insights into the structural conditions. Nowadays, high-resolution wavefield imaging has been widely studied and applied in damage identification. However, full wavefield imaging techniques have some limitations, including slow data acquisition and lack of accuracy. The objectives of this dissertation are to develop novel and high resolution Guided Wavefield Imaging techniques able to detect defects in metals and composite materials while reducing the acquisition time without losing in detection accuracy

Ultrasonic guided wave imaging via sparse reconstruction

Structural health monitoring (SHM) is concerned with the continuous, long-term assessment of structural integrity. One commonly investigated SHM technique uses guided ultrasonic waves, which travel through the structure and interact with damage. Measured signals are then analyzed in software for detection, estimation, and characterization of damage. One common configuration for such a system uses a spatially-distributed array of fixed piezoelectric transducers, which is inexpensive and can cover large areas. Typically, one or more sets of prerecorded baseline signals are measured when the structure is in a known state, with imaging methods operating on differences between follow-up measurements and these baselines. Presented here is a new class of SHM spatially-distributed array algorithms that rely on sparse reconstruction. For this problem, damage over a region of interest (ROI) is considered to be sparse. Two different techniques are demonstrated here. The first, which relies on sparse reconstruction, uses an a priori assumption of scattering behavior to generate a redundant dictionary where each column corresponds to a pixel in the ROI. The second method extends this concept by using multidimensional models for each pixel, with each pixel corresponding to a "block" in the dictionary matrix; this method does not require advance knowledge of scattering behavior. Analysis and experimental results presented demonstrate the validity of the sparsity assumption. Experiments show that images generated with sparse methods are superior to those created with delay-and-sum methods; the techniques here are shown to be tolerant of propagation model mismatch. The block-sparse method described here also allows the extraction of scattering patterns, which can be used for damage characterization.Ph.D

Sparse and Dispersion-Based Matching Pursuit for Minimizing the Dispersion Effect Occurring when Using Guided Wave for Pipe Inspection

Ultrasonic guided wave is an effective tool for structural health monitoring of structures for detecting defects. In practice, guided wave signals are dispersive and contain multiple modes and noise. In the presence of overlapped wave-packets/modes and noise together with dispersion, extracting meaningful information from these signals is a challenging task. Handling such challenge requires an advanced signal processing tool. The aim of this study is to develop an effective and robust signal processing tool to deal with the complexity of guided wave signals for non-destructive testing (NDT) purpose. To achieve this goal, Sparse Representation with Dispersion Based Matching Pursuit (SDMP) is proposed. Addressing the three abovementioned facts that complicate signal interpretation, SDMP separates overlapped modes and demonstrates good performance against noise with maximum sparsity. With the dispersion taken into account, an overc-omplete and redundant dictionary of basic atoms based on a narrowband excitation signal is designed. As Finite Element Method (FEM) was used to predict the form of wave packets propagating along structures, these atoms have the maximum resemblance with real guided wave signals. SDMP operates in two stages. In the first stage, similar to Matching Pursuit (MP), the approximation improves by adding, a single atom to the solution set at each iteration. However, atom selection criterion of SDMP utilizes the time localization of guided wave reflections that makes a portion of overlapped wave-packets to be composed mainly of a single echo. In the second stage of the algorithm, the selected atoms that have frequency inconsistency with the excitation signal are discarded. This increases the sparsity of the final representation. Meanwhile, leading to accurate approximation, as discarded atoms are not representing guided wave reflections, it simplifies extracting physical meanings for defect detection purpose. To verify the effectiveness of SDMP for damage detection results from numerical simulations and experiments on steel pipes are presented

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