Low-frequency vibrations typically occur in many practical structures and systems when in use, for example, in aerospaces and industrial machines. Piezoelectric materials feature compactness, lightweight, high integration potential, and permit to transduce mechanical energy from vibrations into electrical energy. Because of their properties, piezoelectric materials have been receiving growing interest during the last decades as potential vibration- harvested energy generators for the proliferating number of embeddable wireless sensor systems in applications such as structural health monitoring (SHM). The basic idea behind piezoelectric energy harvesting (PEH) powered architectures, or energy harvesting (EH) more in general, is to develop truly “fit and forget” solutions that allow reducing physical installations and burdens to maintenance over battery-powered systems. However, due to the low mechanical energy available under low-frequency conditions and the relatively high power consumption of wireless sensor nodes, PEH from low-frequency vibrations is a challenge that needs to be addressed for the majority of the practical cases. Simply saying, the energy harvested from low-frequency vibrations is not high enough to power wireless sensor nodes or the power consumption of the wireless sensor nodes is higher than the harvested energy. This represents a main barrier to the widespread use of PEH technology at the current state of the development, despite the advantages it may offer. The main contribution of this research work concerns the proposal of a novel EH circuitry, which is based on a whole-system approach, in order to develop enhanced PEH powered wireless sensor nodes, hence to compensate the existing mismatch between harvested and demanded energy. By whole-system approach, it is meant that this work develops an integrated system-of-systems rather than a single EH unit, thus getting closer to the industrial need of a ready- to-use energy-autonomous solution for wireless sensor applications such as SHM. To achieve so, this work introduces: Novel passive interfaces in connection with the piezoelectric harvester that permit to extract more energy from it (i.e., a complex conjugate impedance matching (CCIM) interface, which uses a PC permalloy toroidal coil to achieve a large inductive reactance with a centimetre- scaled size at low frequency; and interfaces for resonant PEH applications, which exploit the harvester‟s displacement to achieve a mechanical amplification of the input force, a magnetic and a mechanical activation of a synchronised switching harvesting on inductor (SSHI) mechanism). A novel power management approach, which permits to minimise the power consumption for conditioning the transduced signal and optimises the flow of the harvested energy towards a custom-developed wireless sensor communication node (WSCN) through a dedicated energy-aware interface (EAI); where the EAI is based on a voltage sensing device across a capacitive energy storage. Theoretical and experimental analyses of the developed systems are carried in connection with resistive loads and the WSCN under excitations of low frequency and strain/acceleration levels typical of two potential energy- autonomous applications, that are: 1) wireless condition monitoring of commercial aircraft wings through non-resonant PEH based on Macro-Fibre Composite (MFC) material bonded to aluminium and composite substrates; and wireless condition monitoring of large industrial machinery through resonant PEH based on a cantilever structure. shown that under similar testing conditions the developed systems feature a performance in comparison with other architectures reported in the literature or currently available on the market. Power levels up to 12.16 mW and 116.6 µW were respectively measured across an optimal resistive load of 66 277 kΩ for an implemented non-resonant MFC energy harvester on aluminium substrate and a resonant cantilever-based structure when no interfaces were added into the circuits. When the WSCN was connected to the harvesters in place of the resistive loads, data transmissions as fast as 0.4 and s were also respectively measured. By use of the implemented passive interfaces, a maximum power enhancement of around 95% and 452% was achieved in the two tested cases and faster data transmissions obtained with a maximum percentage improvement around 36% and 73%, respectively. By the use of the EAI in connection with the WSCN, results have also shown that the overall system‟s power consumption is as low as a few microwatts during non- active modes of operation (i.e., before the WSCN starts data acquisition and transmission to a base station). Through the introduction of the developed interfaces, this research work takes a whole-system approach and brings about the capability to continuously power wireless sensor nodes entirely from vibration-harvested energy in time intervals of a few seconds or fractions of a second once they have been firstly activated. Therefore, such an approach has potential to be used for real-world energy- autonomous applications of SHM.

Hydraulic systems are used in many fields of applications for different functions like energy storage in hybrid systems. Generally the mass of hydraulic systems plays a key role especially for mobile hydraulics (construction machines, trucks, cars) and hydraulic aircraft systems. The main product properties like energy efficiency or payload can be improved by reducing the mass. In this connection carbon fiber reinforced plastics (CFRP) with their superior specific strength and stiffness open up new chances to acquire new lightweight potentials compared to metallic components. However, complex quality control and failure identification slow down the substitution of metals by fiber-reinforced plastics (FRP). But the lower manufacturing temperatures of FRP compared to metals allow the integration of sensors within FRP-components. These sensors then can be advantageously used for many functions like quality control during the manufacturing process or structural health monitoring (SHM) for failure detection during their life cycle. Thus, lightweight hydraulic components made of composite materials as well as sensor integration in composite components are a main fields of research and development at the Institute of Lightweight Engineering and Polymer Technology (ILK) of the TU Dresden as well as at the Leichtbau-Zentrum Sachsen GmbH (LZS).

Radiation damage occurs in reactor pressure vessel (RPV) steel, causing microstructural changes such as point defect clusters, interstitial loops, vacancy-solute clusters, and precipitates, that cause material embrittlement. Radiation damage is a crucial concern in the nuclear industry since many nuclear plants throughout the US are entering the first period of life extension and older plants are currently undergoing assessment of technical basis to operate beyond 60 years. The result of extended operation is that the RPV and other components will be exposed to higher levels of neutron radiation than they were originally designed to withstand. There is currently no nondestructive evaluation technique that can unambiguously assess the amount of radiation damage in RPV steels. Nonlinear ultrasound (NLU) is a nondestructive evaluation technique that is sensitive to microstructural features such as dislocations, precipitates, and their interactions in metallic materials. The physical effect monitored by NLU is the generation of higher harmonic frequencies in an initially monochromatic ultrasonic wave, arising from the interaction of the ultrasonic wave with microstructural features. This effect is quantified with the measurable acoustic nonlinearity parameter, beta. In this work, nonlinear ultrasound is used to characterize radiation damage in reactor pressure vessel steels over a range of fluence levels, irradiation temperatures, and material composition. Experimental results are presented and interpreted with newly developed analytical models that combine different irradiation-induced microstructural contributions to the acoustic nonlinearity parameter.

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

Résumé : Les systèmes de surveillance de santé structurale sont proposés pour la détection d’endommagement dans les infrastructures qui dépassent leur durée de vie en utilisant les ondes guidées (GW). Les ondes guidées peuvent parcourir de longues distances et sont sensibles à une variété d’imperfections. Les transducteurs piézoélectriques sont communément utilisés pour générer et mesurer les ondes guidées dans des structures minces. Comme la détection du défaut et sa localisation sont souhaitées, la nature de la génération des ondes guidées sous forme de plusieurs modes implique une complexité supérieure dans le traitement du signal. Pour remédier à cette limitation, une nouvelle méthode est présentée ici pour la génération des ondes guidées par sélection de mode, et un nouveau transducteur piézoélectrique est ensuite conçu, fabriqué et testé. Tout d'abord, la génération des ondes guidées par optimisation systématique du profil interfacial de la contrainte de cisaillement en mode sélectif est étudiée. En utilisant le principe de superposition, une méthode d'analyse est d'abord développée pour la modélisation de la génération des ondes guidées par un nombre fini de segments de contrainte de cisaillement uniforme, chacun contribuant à un profil élémentaire d’une contrainte constante de cisaillement. Sur cette base, deux fonctions coût sont définies afin de minimiser les modes indésirables et amplifier le mode sélectionné et le problème d'optimisation est résolu avec un cadre d'optimisation d’algorithme génétique parallèle. Les avantages de cette méthode par rapport à d'autres approches de conception de transducteurs classiques sont (1) la contrainte de cisaillement peut être explicitement optimisée à la fois pour exciter un mode et supprimer d'autres modes indésirables, (2) la taille de la zone d'excitation n’est pas limitée et l’excitation en mode sélectif est toujours possible, même si la largeur d'excitation est inférieure à toutes les longueurs d'onde excitées, et (3) la sélectivité est accrue et la largeur de bande est étendue. La méthode analytique et les fonctions coût sont ensuite développées pour concevoir un transducteur piézoélectrique à éléments multiples (MEPT) simple et performant. Une méthode numérique est tout d'abord mise au point pour extraire la contrainte interfaciale entre un seul élément piézocéramique et une structure d'accueil et ensuite utilisée comme entrée d'un modèle analytique pour prédire la propagation des ondes guidées à travers l'épaisseur d'une plaque isotrope. Deux nouvelles fonctions coût sont proposées pour optimiser la contrainte de cisaillement interfaciale pour supprimer le(s) mode(s) indésirable(s) et maximiser un mode désiré. Simplicité et faible coût de fabrication sont deux principales cibles visées dan la conception du MEPT. Un prototype TPEM est ensuite fabriqué à l'aide de micro-usinage laser. Une procédure expérimentale est présentée afin de valider les performances de la TPEM comme une nouvelle solution pour la génération des ondes guidées en mode sélectif. Des essais expérimentaux illustrent la forte capacité du TPEM pour la génération des ondes guidées en mode sélectif, puisque le mode indésirable est supprimé par un facteur allant jusqu'à 170 fois par rapport aux résultats obtenus avec un seul piézocéramique. / Abstract : Structural Health Monitoring (SHM) systems are being proposed for damage detection of infrastructures that exceed their life using ultrasonic Guided waves (GWs). GWs can travel over long distances and are sensitive to variety of defects. Piezoelectric transducers (PZTs) are commonly used to generate and measure GWs in plate-like structures. As damage detection and localization is sought, the multi-mode nature of GW generation involves higher complexity in signal processing. To overcome this limitation, a new method is presented here for modeselective GW generation, and a novel mode-selective PZT is then designed, manufactured and tested. First, mode-selective generation of GWs by systematic optimization of the interfacial shear stress profile is investigated. Using the superposition principle, an analytical method is first developed for modeling GWs generation by a finite number of uniform shear stress segments, each contributing with a constant elementary shear stress profile. Based on this, two cost functions are defined in order to minimize the undesired modes and amplify the selected mode and the optimization problem is solved with a parallel Genetic Algorithm (GA) optimization framework. Advantages of this method over more conventional transducers tuning approaches are that (1) the shear stress can be explicitly optimized to both excite one mode and suppress other undesired modes, (2) the size of the excitation area is not constrained and mode-selective excitation is still possible even if excitation width is smaller than all excited wavelengths, and (3) the selectivity is increased and the bandwidth extended. The analytical method and objective functions are then developed to design a novel and costeffective multi-element piezoelectric transducer (MEPT). A numerical method is first developed to extract the interfacial stress between a single piezoceramic element and a host structure and then used as the input of an analytical model to predict the GW propagation through the thickness of an isotropic plate. Two novel objective functions are proposed to optimize the interfacial shear stress for both suppressing unwanted mode(s) and maximizing a desired mode. Simplicity and low manufacturing cost are two main targets driving the design of the MEPT. A prototype MEPT is then manufactured using laser micro-machining. An experimental procedure is presented to validate the performances of the MEPT as a new solution for mode-selective GW generation. Experimental tests illustrate the high capability of the MEPT for mode-selective GW generation, as unwanted mode is suppressed by a factor up to 170 times compared with the results obtained with a single piezoceramic.

<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>

<p>In recent years, there has been an increase in the popularity of wearable sensors such as electroencephalography (EEG) sensors, electromyography (EMG) sensors, gyroscopes, accelerometers, and photoplethysmography (PPG) sensors. This work is focused on PPG sensors, which are used to measure heart rate in real time. They are currently used in many commercial products such as Fitbit Watch and Muse Headband. Due to their low cost and relative implementation simplicity, they are easy to add to custom-built wearable devices.</p><p><br></p> <p>We built an Arduino-based wearable wrist sensor-kit that consists of a PPG sensor in addition to other low cost commercial biosensors to measure biosignals such as pulse rate, skin temperature, skin conductivity, and hand motion. The purpose of the sensor-kit is to analyze the effects of stress on students in a classroom based on changes in their biometric signals. We noticed some failures in the measured PPG signal, which could negatively affect the accuracy of our analysis. We conjectured that one of the causes of failure is movement. Therefore, in this thesis, we build automatic failure detection methods and use these methods to study the effect of movement on the signal.</p><p><br></p> <p>Using the sensor-kit, PPG signals were collected in two settings. In the first setting, the participants were in a still sitting position. These measured signals were manually labeled and used in signal analysis and method development. In the second setting, the signals were acquired in three different scenarios with increasing levels of activity. These measured signals were used to investigate the effect of movement on the reliability of the PPG sensor. </p><p><br></p> <p>Four types of failure detection methods were developed: Support Vector Machines (SVM), Deep Neural Networks (DNN), K-Nearest Neighbor (K-NN), and Decision Trees. The classification accuracy is evaluated by comparing the resulting Receiver Operating Characteristic (ROC) curves, Area Above the Curve (AAC), as well as the duration of failure and non-failure sequences. The DNN and Decision Tree results are found to be the most promising and seem to have the highest error detection accuracy. </p> <p> </p> <p>The proposed classifiers are also used to assess the reliability of the PPG sensor in the three activity scenarios. Our findings indicate that there is a significant presence of failures in the measured PPG signals at rest, which increases with movement. They also show that it is hard to obtain long sequences of pulses without failure. These findings should be taken into account when designing wearable systems that use heart rate values as input.</p>

<div> Recent advancements of health monitoring sensing technologies are enabling plethora of new applications in a variety of biomedical areas. In this work, we present a new sensing technology that enables a fully autonomous monitoring of urinary tract infection (UTI). UTI is the second most common infection in the human body caused by bacterial pathogens, and costs millions of dollars each year to the patients and the health care industry. UTI is easily treatable using antibiotics if identified in early stages. However, when early stage identification is missed, UTI can be a major source of serious complications such as ascending infections, loss of kidney function, bacteremia, and sepsis. Unfortunately, the limitations of existing UTI monitoring technologies such as high cost, time-intensive sample preparation, and relatively high false alarm rate prohibit reliable detection of UTI in early stages. The problem becomes more serious in certain patient groups such as infants and geriatric patients suffering from neurodegenerative diseases, who have difficulties in realizing the symptoms and communicating the symptoms with their caregivers. In addition to the aforementioned difficulties, the fact that UTI is often asymptomatic makes early stage identifications quite challenging, and the reliable monitoring and detection of UTI in early stages remain as a serious problem.</div><div> To address these issues, we propose a diaper-embedded, self-powered, and fully autonomous UTI monitoring sensor module that enables autonomous monitoring and detection of UTI in early stages with minimal effort. The sensor module consists of a paper-based colorimetric nitrite sensor, urine-activated batteries, a boost dc-dc converter, a low-power sensor interface utilizing pulse width modulation, a Bluetooth low energy module for wireless transmission, and a software performing calibration at run-time. </div><div> To further optimize the sensor module, a new fully integrated DC-DC converter with low-profile and low ripple is developed. The proposed DC-DC converter maintains an extremely low level of output voltage ripples in the face of different battery output voltages, which is crucial for realizing low-noise sensor interfaces. Since the DC-DC converter is a part of a module embedded into a diaper, it is highly desirable for the DC-DC converter to have a small physical form factor in both area and height. To address this issue, the proposed DC-DC converter adopts a new charge recycling technique that enables a fully integrated design without utilizing any off-chip components. In addition, the DC-DC converter utilizes sub-module sharing techniques – multiple modules share a voltage buffer and a recycle capacitor to reduce power consumption and save chip area. The DC-DC converter provides a regulated voltage of 1.2V and achieves a maximum efficiency of 80% with a 300ohm load resistance. The output voltage ripple is in the range of 19.6mV to 26.6mV for an input voltage ranging from 0.66 to 0.86V.<br></div>

Le sujet de thèse propose une nouvelle génération de réseaux de capteur sans fil base sur impulse radio ultra wide band (IR-UWB) reconfigurable suivant l'application souhaitée et à très basse consommation. La consommation énergétique d’un système de communication sans fil est la contrainte majeure pour le déploiement d’un réseau de capteurs sans fil autonome. Les travaux de recherche présente dans cette thèse ont menés au développement d’un émetteur-récepteur à très faible consommation d’énergie pour les réseaux de capteurs sans fil autonome pour des applications de structural Heath monitoring dans des domaines aéronautique. Une description est faite pour les différents types de technologie de communication sans fil pour la surveillance des structures (SHM). Nous avons détaillé la communication sans fil ultra large bande (UWB) en présentant la technique de communication sans fil UWB par impulsion avec les avantages qu’elle offre pour notre application. Une présentation est faite de l’architecture de l’émetteur-récepteur IR-UWB conçu en détaillant le design complet avec l’intégration de la solution proposée clock-gating pour un système à une grande efficacité énergétique avec une implémentation et validation d’un prototype sur une plateforme FPGA. Une description de la conception et la fabrication d’un système sur puce ASIC de notre design d’émetteur-récepteur IR-UWB avec la technologie CMOS 65nm de st microélectronique et les avantages qu’il offre que ça soit en terme d’efficacité énergétique ou de taille de système. / This Ph.D. Subject proposes the design of a new generation of wireless sensor networks (WSN) based on impulse radio ultra-wide band (IR-UWB), reconfigurable upon the application, reliable and ultra-low power. Applications like structure health monitoring of aerospace structures or portable smart sensing systems for human protection can be targeted. These industrial applications impose very demanding specifications for the wireless communication protocol (in some cases, new services are needed like: localization, clock synchronization, real-time transmission, etc) on one side, and for the circuit design, on the other side, as the ultra-low power circuits are needed. Energy efficiency is the major driver in today development of the wireless sensor networks. We chose impulse radio ultra-wideband (IR-UWB) technique for our developments. IR-UWB is a very promising technique able to respond to the wireless communication protocol constraints and to energy efficiency constraints.! The objective of this Ph.D. will be to design an ultra-low power IR-UWB transceiver. IR-UWB signal processing techniques has to be study and innovator solution has to be proposed for the implementation of the IR-UWB transceiver. The first prototype will be developed on FPGA boards (and/or USRP boards) and the final IR-UWB transceiver will be an ASIC in CMOS technology. The design of an ultra-low power consumption of the CMOS transceiver will be a major concern. Modern ultra-low power circuit techniques from the nanometrics CMOS design kits will be used. MAC layer adapted to the demands of the application and working on IR-UWB physical layer will be also studied and designed. A microprocessor integration on the chip for power management of the different parts (sensor, communication, computing, energy harvesting) of the system can also be studied. This work will be based on the previous research results obtained in our team in the case of static WSN. This work will take plac! e in the highly stimulating and competitive environment of a E! uropean project.

Orientador: Fábio Roberto Chavarette / Resumo: Este trabalho é dedicado ao desenvolvimento de uma metodologia baseada no monitoramento da integridade estrutural em aeronaves com foco em técnicas de computação inteligente, tendo como intuito detectar, localizar e quantificar falhas estruturais utilizando os sistemas imunológicos artificiais (SIA). Este conceito permite compor o sistema de diagnóstico apto a aprender continuamente, contemplando distintas situações de danos, sem a necessidade de reiniciar o processo de aprendizado. Neste cenário, foi empregado dois algoritmos imunológicos artificiais, sendo o algoritmo de seleção negativa, responsável pelo processo de reconhecimento de padrões, e o algoritmo de seleção clonal responsável pelo processo de aprendizado continuado. Também foi possível quantificar o grau de influência do dano para as cinco situações de danos. Para avaliar a metodologia foi montada uma bancada experimental com transdutores piezelétricos que funcionam como sensor e atuador em configurações experimentais, que podem ser anexadas à estrutura para produzir ou coletar ondas numa placa de alumínio (representando a asa do avião), sendo coletados sinais na situação normal e em cinco situações distintas de danos. Os resultados demonstraram robustez e precisão da nova metodologia proposta. / Abstract: This work is dedicated to the development of a methodology based on the monitoring of structural integrity in aircraft with a focus on intelligent computing techniques, aiming to detect structural failures using the artificial immune systems (AIS). This concept allows to compose the diagnostic system capable of learning continuously, contemplating different situations of damages, without the need to restart the learning process. In this scenario, two artificial immunological algorithms were employed, the negative selection algorithm, responsible for the pattern recognition process, and the clonal selection algorithm responsible for the continuous learning process. It was also possible to quantify the degree of influence of the damage for the five damage situations. To assess the methodology, an experimental bench was mounted with piezoelectric transducers that act as sensors and actuators in experimental configurations, which can be attached to the structure to produce or collect waves on an aluminum plate (representing the wing of the airplane), being collected signals in the normal situation and in five different situations of damages. The results demonstrate the robustness and accuracy of the proposed new methodology. / Doutor