There is a need in exploring structural health monitoring technologies for the composite structures particularly aged Composite Overwrapped Pressure Vessels (COPVs) for the current and future implementation of COPVs for space missions. In this study, the research was conducted in collaboration with NASA Kennedy Space Center and also NASA Marshall Space and Flight Center engineers. COPVs have been used to store inert gases like helium (for propulsion) and nitrogen (for life support) under varying degrees of pressure onboard the orbiter since the beginning of the Space Shuttle Program. After the Columbia accident, the COPVs were re-examined and different studies (e.g. Laser profilometry inspection, NDE utilizing Raman Spectroscopy) have been conducted and can be found in the literature. To explore some of the unique in-house developed hardware and algorithms for monitoring COPVs, this project is carried out with the following general objectives: 1) Investigate the obtaining indices/features related to the performance and/or condition of pressure vessels 2) Explore different sensing technologies and Structural Health Monitoring (SHM) systems 3) Explore different types of data analysis methodologies to detect damage with particular emphasis on statistical analysis, cross-correlation analysis and Auto Regressive model with eXogeneous input (ARX) models 4) Compare differences in various types of pressure vessels First an introduction to theoretical pressure vessels, which are used to compare to actual test specimens, is presented. Next, a background review of the test specimens including their applications and importance is discussed. Subsequently, a review of related SHM applications to this study is presented. The theoretical background of the data analysis methodologies used to detect damage in this study are provided and these methodologies are applied in the laboratory using Composite Overwrapped Pressure Vessels (COPVs) to determine the effectiveness of these techniques. Next another study on the Air Force Research Laboratory (AFRL) Tank that is carried out in collaboration with NASA KSC and NASA MSFC is presented with preliminary results. Finally the results and interpretations of both studies are summarized and discussed.

In the field of structural engineering it is of vital importance that the condition of an ageing structure is monitored to detect damages that could possibly lead to failure of the structure. Over the past few years various methods for monitoring the condition of structures have been proposed. With respect to civil and mechanical structures several methods make use of modal parameters such as, natural frequency, damping ratio and mode shapes. In the present work four methods for modal parameter estimation and two methods for have been evaluated for their application to multi degree of freedom structures. The methods evaluated for modal parameter estimation are: Wavelet transform, Hilbert-Huang transform, parametric system identification and peak picking. Through various numerical simulations the effectiveness of these methods is studied. It is found that the simple peak-picking method performs the best and is able to identify modal parameters most accurately in all the simulation cases that were considered in this study. The identified modal parameters are then used for locating the damage. Herein the flexibility and the rotational flexibility approaches are evaluated for damage detection. The approach based on the rotational flexibility is found to be more effective. / Master of Science

One of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea, the state of the art technique in the field is straight forward - populating a given structure with sensors and extracting information from them. In this regard, instrumenting with too many sensors may be inefficient as this could lead to superfluous data that is expensive to capture and process. This research aims to explore an alternate SHM technique that optimizes the data acquisition process by eliminating the amount of redundant data that is sensed and uses this sufficient data to detect and locate the fault present in the structure. Efficient data acquisition requires a mechanism that senses just the necessary amount of data for detection and location of fault. For this reason Compressive Sensing (CS) is explored as a plausible idea. CS claims that signals can be reconstructed from what was previously believed to be incomplete information by Shannon's theorem, taking only a small amount of random and linear non - adaptive measurements. As responses of many physical systems contain a finite basis, CS exploits this feature and determines the sparse solution instead of the traditional least - squares type solution.As a first step, CS is demonstrated by successfully recovering the frequency components of a simple sinusoid. Next, the question of how CS compares with the conventional Fourier transform is analyzed. For this, recovery of temporal frequencies and signal reconstruction is performed using the same number of samples for both the approaches and the errors are compared. On the other hand, the FT error is gradually minimized to match that of CS by increasing the number of regularly placed samples. Once the advantages are established, feasibility of using CS to detect damage in a single degree of freedom system is tested under unforced and forced conditions. In the former scenario, damage is indicated when there is a change in natural frequency of vibration of the system after an impact. In the latter, the system is excited harmonically and damage is detected by a change in amplitude of the system's vibration. As systems in real world applications are predominantly multi-DOF, CS is tested on a 2-DOF system excited with a harmonic forcing. Here again, damage detection is achieved by observing the change in the amplitude of vibration of the system. In order to employ CS for detecting either a change in frequency or amplitude of vibration of a structure subjected to realistic forcing conditions, it would be prudent to explore the reconstruction of a signal which contains multiple frequencies. This is accomplished using CS on a chirp signal. Damage detection is clearly a spatio-temporal problem. Hence it is important to additionally explore the extension of CS to spatial reconstruction. For this reason, mode shape reconstruction of a beam with standard boundary conditions is performed and validated with standard/analytical results from literature. As the final step, the operation deflection shapes (ODS) are reconstructed for a simply supported beam using CS to establish that it is indeed a plausible approach for a less expensive SHM. While experimenting with the idea of spatio-temporal domain, the mode shape as well as the ODS of the given beam are examined under two conditions - undamaged and damaged. Damage in the beam is simulated as a decrease in the stiffness coefficient over a certain number of elements. Although the range of modes to be examined heavily depends on the structure in question, literature suggests that for most practical applications, lower modes are more dominant in indicating damage. For ODS on the other hand, damage is indicated by observing the shift in the recovered spatial frequencies and it is confirmed by the reconstructed response.

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Electrical impedance tomography (EIT) is a non-destructive, non-invasive, and non-radioactive imaging technique used for reconstructing the internal conductivity distribution of a sensing domain. Performing EIT often requires large, stationary benchtop equipment that can be expensive and impractical. Other researchers have attempted to make portable EIT systems, but they all rely on external computation for image reconstruction/data analysis. This study outlines the development of a low-cost, portable, and wireless EIT data acquisition (DAQ) system that is capable of independently performing image reconstructions on-board. With the proposed system, EIT can be performed on carbon fiber reinforced polymers to spatially locate damages. Since EIT reconstruction algorithms can be extremely computationally intensive, this study has also developed an alternative deep-learning algorithm that leverages the compressed-sensing technique to strategically train a neural network. The proposed neural network has not only achieved comparable results to traditional iterative algorithms, but it can do so in a fraction of the time.

Master of Science / Department of Civil Engineering / Hani G. Melhem / The objective of this research is to use structure ambient random vibration response to detect damage level and location. The use of ambient vibration is advantageous because excitation is caused by service conditions such as normal vehicle traffic on a highway bridge, train passage on a railroad bridge, or wind loads on a tall building. This eliminates the need to apply a special impact or dynamic load, or interrupt traffic on a bridge in regular service. This research developed an approach in which free vibration of a structure is extracted from the response of this structure to a random excitation in the time domain (acceleration versus time) by averaging out the random component of the response. The result is the free vibration that includes all modes based on the sampling rate on time. Then this free vibration is transferred to the frequency domain using a Fast Fourier Transform (FFT). Variations in frequency response are a function of structural stiffness and member end-conditions. Such variations are used as a measure to identify the change in the structural dynamic properties, and ultimately detect damage. A physical model consisting of a 20 × 20 × 1670 -mm long steel square tube was used to validate this approach. The beam was tested under difference supports conditions varying from a single- to three-span continuous configuration. Random excitation was applied to the beam, and the dynamic response was measured by an accelerometer placed at various locations on the span. A numerical model was constructed in ABAQUS and the dynamic response was obtained from the finite element model subjected to similar excitation as in the physical model. Numerical results were correlated against results from the physical model, and comparison was made between the different span/support configurations. A subsequent step would be to induce damage that simulates loss of stiffness or cracking condition of the beam cross section, and that would be reflected as a change in the frequency and other dynamic properties of the structure. The approach achieved good results for a structure with a limited number of degrees of freedom. Further research is needed for structures with a larger number of degrees of freedom and structures with damage in symmetrical locations relative to the accelerometer position.

Le paradigme de la surveillance de santé structurale repose sur l'introduction d'indicateurs fiables et robustes permettant de détecter, localiser, quantifier et prédire un endommagement de manière précoce. En effet, la détection d'une modification structurale susceptible de devenir critique peut éviter l'occurrence de dysfonctionnements majeurs associés à des conséquences sociales, économiques et environnementales très importantes.Ces dernières années, de nombreuses recherches se fait de l'évaluation dynamique un élément de diagnostic. La plupart des méthodes reposent sur une analyse temporelle ou fréquentielle des signaux pour en extraire une information compressée au travers de quelques caractéristiques modales ou d'indicateurs évolués construits sur ces caractéristiques. Ces indicateurs ont montré leur efficacité, mais le problème de leur sensibilité, de la nécessité de disposer d'un état de référence, et de leur fiabilité en terme de la probabilité de détection et de fausses alarmes, reste entier. De plus, le fait d'utiliser des mesures dynamiques (particulièrement si plusieurs voies de mesures sont considérées) mène au stockage de grands volumes de données.Dans ce contexte, il est important d'employer des techniques permettant d'utiliser autant des données brutes que les propriétés modales de manière pratique et pertinente. Pour cela, des représentations adaptées ont été développées pour améliorer la manipulation et le stockage des données. Ces représentations sont connues sous le nom de og données symboliques fg . Elles permettent de caractériser la variabilité et l'incertitude qui entachent chacune des variables. Le développement de nouvelles méthodes d'analyse adéquates pour traiter ces données est le but de l'Analyse de Données Symboliques (ADS).L'objectif de cette thèse est double : le premier consiste à utiliser différentes méthodes couplées à l'ADS pour détecter un endommagement structural. L'idée est d'appliquer des procédures de classification non supervisée (e.g. divisions hiérarchiques, agglomérations hiérarchiques et nuées dynamiques) et supervisée (e.g., arbres de décision Bayésiens, réseaux de neurones et machines à vecteurs supports) afin de discriminer les différents états de santé d'une structure. Dans le cadre de cette thèse, l'ADS est appliquée aux mesures dynamiques acquises emph{in situ} (accélérations) et aux paramètres modaux identifiés. Le deuxième objectif est la compréhension de l'impact des effets environnementaux, notamment de ceux liés à la variation thermique, sur les paramètres modaux. Pour cela, des techniques de régression des données sont proposées.Afin d'évaluer la pertinence des démarches proposées, des études de sensibilité sont menées sur des exemples numériques et des investigations expérimentales. Il est montré que le couplage de l'ADS aux méthodes de classification de données permet de discriminer des états structuraux avec un taux de réussite élevé. Par ailleurs, la démarche proposée permet de vérifier l'importance d'utiliser des techniques permettant de corriger les propriétés modales identifiées des effets thermiques, afin de produire un processus de détection d'endommagements efficace / The paradigm of structural health monitoring is based on the development of reliable and robust indicators able to detect, locate, quantify and predict damage. Studies related to damage detection in civil engineering structures have a noticeable interest for researchers in this area. Indeed, the detection of structural changes likely to become critical can avoid the occurrence of major dysfunctions associated with social, economic and environmental consequences.Recently, many researches have focused on dynamic assessment as part of structural diagnosis. Most of the studied techniques are based on time or frequency domain analyses to extract compressed information from modal characteristics or based on indicators built from these parameters. These indicators have shown their potentialities, but the problem of their sensitivity, the necessity of a reference state, and their reliability in terms of detection probability and false alarm, still remains. Moreover, the use of raw dynamic measurements (especially if several measurement channels are considered) leads to the storage of large datasets.In this context, it is important to use techniques capable of dealing not only with raw data but also modal parameters in a practical and relevant way. In order to give some insights to this problem, appropriate representations have been developed to improve both manipulation and storage of data. These representations are known as og symbolic data fg. They are used to characterize the variability and uncertainty that exists within each variable. The development of new methods capable of dealing with this type of data is the goal of Symbolic Data Analysis (SDA).This thesis has two main objectives: the first one is to use different methods coupled with the SDA to detect structural damage. The idea is to employ clustering procedures (e.g., hierarchy-divisive, hierarchy-agglomerative and dynamic clouds) and supervised classification methods (e.g., Bayesien decision trees, neural networks and support vector machines) to discriminate different structural states. In this thesis, SDA is applied to dynamic measurements obtained on site (accelerations) and to the identified modal parameters. The second goal is to study the impact of environmental effects, particularly those related to thermal variation over modal parameters. To this end, a couple of regression techniques are proposed.In order to attest the efficiency of the proposed approaches, several sensibility studies considering numerical applications and experimental investigations are carried out. It is shown that SDA coupled with classification methods is able to distinguish structural conditions with adequate rates. Furthermore, it is stressed the importance of using techniques capable of correcting modal parameters from thermal effects in order to build efficient procedures for damage detection

<div> <p>Fiber reinforced polymer composites are widely used in manufacturing advanced light weight structures for the aerospace, automotive, and energy sectors owing to their superior stiffness and strength. With the increasing use of composites, there is an increasing need to monitor the health of these structures during their lifetime. Currently, health monitoring in filament wound composites is facilitated by embedding piezoelectrics and optical fibers in the composite during the manufacturing process. However, the incorporation of these sensing elements introduces sites of stress concentration which could lead to progressive damage accumulation. In addition to introducing weak spots in the structure, they also make the manufacturing procedure difficult. </p> <p> </p> <p>Alternatively, nanofiller modification of the matrix imparts conductivity which can be leveraged for real time health monitoring with fewer changes to the manufacturing method. Well dispersed nanofillers act as an integrated sensing network. Damage or strain severs the well-connected nanofiller network thereby causing a local change in conductivity. The self-sensing capabilities of these modified composites can be combined with low cost, minimally invasive imaging modalities such as electrical impedance tomography (EIT) for damage detection. To date, however, EIT has exclusively been used for damage detection in planar coupons. These simple plate-like structures are not representative of real-world complex geometries. This thesis advances the state of the art in conductivity-based structural health monitoring (SHM) and nondestructive evaluation (NDE) by addressing this limitation of EIT. The current study will look into damage detection of a non-planar multiply connected domain – a filament-wound glass fiber/epoxy tube modified by carbon black (CB) filler. The results show that EIT is able to detect through holes as small as 7.94 mm in a tube with length-to-diameter ratio of 132.4 mm-to-66.2 mm (aspect ratio of 2:1). Further, the sensitivity of EIT to damage improved with decreasing tube aspect ratio. EIT was also successful in detecting sub-surface damage induced by low velocity impacts. These results indicate that EIT has much greater potential for composite SHM and NDE than prevailing work limited to planar geometries suggest.</p> </div> <br>

The future of the aerospace industry in large part relies on two factors: (i) development of advanced damage tolerant materials and (ii) development of advanced smart sensors with the ability to detect and evaluate defects at very early stages of component service life. Laminated composite materials, such as carbon fibre reinforced plastics (CFRP), have emerged as the materials of choice for increasing the performance and reducing the cost and weight of aircrafts, which leads to less fuel consumption and therefore lower CO2 emissions. However, it is well known that these materials exhibit fragile behaviour, poor resistance to impact damage caused by foreign objects and require a relatively slow and labour intensive manufacturing process. These factors prevent the rapid expansion of composite materials in several industrial sectors at the current time. Inspired by the use of rope throughout history and driven by the necessity of creating a lean manufacturing process for composites and enhancing their impact properties, the first part of this work has shown that enhanced damage tolerance and pseudo-ductile behaviour can be achieved with standard CFRP by creatively arranging the fibres into a 3D twisted helical configuration. Through an extensive experimental campaign a new method to arrange fibre reinforcement was presented and its effect investigated. The second part of this PhD work focused on developing a new smart sensor. A spiral passive electromagnetic sensor (SPES) for damage detection on CFRP and glass fibre reinforced plastics (GFRP) is presented in this work. A range of defect types in glass and carbon composite has been considered, such as delamination, perforated holes and cracks. Furthermore, throughout this work, the SPES has been exploited as a multi-sensing device allowing the ability to detect temperature and humidity variation, presence of ice and act as an anti/de-icing device.