Depuis plusieurs années l'équipe de recherche S4M se concentre sur une approche technologique de la SHM avec pour objectif la surveillance de systèmes complexes par des capteurs intelligents distribués: le Smart Sensing. L'équipe S4M conduit des travaux d'instrumentation de structures complexes au travers du déploiement de systèmes de surveillance distribués et de recherche de marqueurs de vieillissement par la mesure et l'exploitation de signaux via des capteurs MEMS déployés. Différents domaines ont déjà été adressés avec des travaux conduits conjointement avec des constructeurs aéronautiques. Ce travail de recherche, effectué en partenariat avec le laboratoire LMDC de l'INSA se focalise sur le matériau de type béton renforcé par des plaques composites, comme structure hétérogène nécessitant une surveillance périodique et/ou continue. Un des enjeux est de contrôler la maintenance préventive ou le surdimensionnement par des coefficients de confiance en proposant une méthode de contrôle non destructif. Notre objectif de recherche est de contribuer dans la recherche d'une ou de signature(s) dans des signaux mesurés par des capteurs piezo en réponse à des impulsions générant la propagation d'ondes mécaniques témoignant un vieillissement ou un endommagement de la structure poutre en béton armé. / For several years the research team S4M focuses on a technological approach to SHM with the aim for monitoring of complex systems by intelligent sensors distributed: Smart Sensing. The S4M team led instrumentation complex structures work through the deployment of distributed monitoring systems and search for markers of aging by measuring and operating signals through deployed MEMS sensors. Different areas have already been addressed with the work conducted jointly with aircraft manufacturers. This research, conducted in partnership with the LMDC-INSA laboratory focuses on the concrete like material reinforced composite plates as heterogeneous structure requiring periodic or continuous monitoring. One of the challenges is to control preventive maintenance or oversizing trusted coefficients by providing a non-destructive testing method. Our research goal is to help in the search for a signature in the signals measured by piezo sensors in response to pulses generating propagation of mechanical waves reflecting an aging or damage to the beam structure of reinforced concrete.

Submitted by Maria Suzana Diniz (msuzanad@hotmail.com) on 2015-11-05T14:36:51Z No. of bitstreams: 1 arquivototal.pdf: 4812802 bytes, checksum: e5276e62e6294058de196c6252bedc95 (MD5) / Made available in DSpace on 2015-11-05T14:36:51Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4812802 bytes, checksum: e5276e62e6294058de196c6252bedc95 (MD5) Previous issue date: 2015-02-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work deals with the evaluation of the communication system by radio frequency using the NRF-24L01+® transceiver to be used in monitoring of electrical systems on the concept of smart grid. The complete system consists of one or more Units Remote Data Acquisition - URDAs; multiple Smart Sensing Units - SSUs; and Supervisory Control Subsystem - SCS. The connection between URAD and SSUs may occur via wired connection (Ethernet, RS232, USB, CAN or PLC) and wireless (RF). URADs fit to the acquisition, processing and communication of variables with low time constant while the USIs are primarily responsible for the acquisition of magnitudes with larger time constants (temperature, pressure, humidity, etc.). In this work, we focus on development and communication of SSUs. For these tests the units are of two types: a master unit, responsible for requesting data (wireless) and sending the SCS (Communication RS232, USB, CAN or RF); and a slave unit, which may account for the measured variables of interest to send to the master unit when requested. For wireless communication (RF), the transceiver nRF - 24L01+® from NORDICTM was used, because its processing characteristics and communication satisfactorily meet the needs and requirements of the project, which will be addressed in the course of this work. / O presente trabalho trata da avaliação do sistema de comunicação por meio de rádio-frequência utilizando o transceiver nRF-24L01+® para ser utilizado no monitoramento de sistemas elétricos no conceito de smart grid. O sistema completo é composto de uma ou mais Unidades Remotas de Aquisição de Dados – URADs; de várias Unidades de Sensoriamento Inteligente – USIs; e um Subsistema de Controle Supervisório – SCS. A conexão entre a URAD e as USIs pode ocorrer através de conexão cabeada (Ethernet, RS232, USB, CAN ou PLC) e sem fio (RF). Cabem às URADs a aquisição, processamento e comunicação das grandezas com pequena constante de tempo, enquanto que as USIs encarregam-se da aquisição de grandezas com constantes de tempo maiores (temperatura, pressão, umidade, etc.). Neste trabalho, tratamos do desenvolvimento e de testes de comunicação da USI. Para estes testes as unidades são de 2 tipos: uma unidade mestre, responsável pela requisição dos dados (sem fio) e pelo envio ao SCS (comunicação RS232, USB, CAN ou RF); e uma unidade escravo, que pode ser responsável pela medição de grandezas de interesse para envio à unidade mestre quando requisitada. Para a comunicação sem fio (RF), utilizou-se o transceptor nRF-24L01+® da NORDICTM, pois suas características de processamento e comunicação atendem satisfatoriamente às necessidades e exigências do projeto, que serão abordadas no transcurso deste trabalho.

Fruit d’un travail collaboratif entre le LAAS-CNRS de Toulouse et l’entreprise MEAS-France / TE Connectivity, ces travaux ont consisté en la mise en place d’une méthodologie permettant la réalisation de capteurs embarqués intelligents utilisant la fusion de données multi-physique pour estimer un paramètre en amoindrissant l’impact des variations environnementales.Nous explorons ici les méthodes liées à la modélisation et l’estimation de paramètres au travers des filtres de Kalman, pour les systèmes linéaires, et des filtres de Kalman étendus (EKF) et Unscented Kalman Filter pour les systèmes non-linéaires. Nous proposons ensuite des méthodes hybrides permettant d’obtenir le meilleur rapport charge de calculs/précision pour les systèmes présentant une évolution linéaire et une mesure non-linéaire.Après une étude de la complexité algorithmique des différentes solutions, nous proposons des méthodes permettant de diminuer la charge de calculs afin de satisfaire les contraintes temps-réel avec une faible puissance de calculs, telles que trouvées couramment dans les applications embarquées. La méthode développée est finalement appliquée sur deux cas applicatifs concrets : le capteur de qualité d’urée de la société MEAS-France/TE Connectivity et le capteur d’analyse du mouvement AREM développés au cours de la thèse au sein du LAAS-CNRS. / The work detailed in this document is the result of a collaborative effort of the LAAS-CNRS in Toulouse and MEAS-France / TE Connectivity during a period of three years.The goal here is to develop a methodology to design smart embedded sensors with the ability to estimate physical parameters based on multi-physical data fusion. This strategy tends to integrate sensors technologies, currently dedicated to lab measurements, in low powered embedded systems working in imperfects environments. After exploring model oriented methods, parameters estimations and Kalman filters, we detail various existing solutions upon which we can build a valid response to multi-physical data fusion problematics, for linear systems with the Kalman Filter, and for non-linear systems with the Extended Kalman Filter and the Unscented Kalman Filter.Then, we will synthesize a filter for hybrid systems, having a linear evolution model and a non-linear measurement model. For example, using the best of the two worlds in order to obtain the best complexity/precision ratio. Once we selected the estimation method, we detail computing power and algorithm complexity problematics in order to find available optimizations we can use to assess the usability of our system in a low power environment. Then we present the developed methodology application to the UQS sensor, sold by TE Connectivity, study case. This sensor uses near infra-red spectroscopy to determine the urea concentration in a urea/water solution, in order to control the nitrogen-oxyde depolluting process in gasoline engines. After a design principles presentation, we detail the model we created in order to represent the system, to simulate its behavior and to combine the measurement data to extract the desired concentration. During this step, we focus on the obstacles of our model calibration and the deviation compensation, due toworking conditions or to components aging process. Based on this development, we finally designed the hybrid models addressing the nominal working cases and the model re-calibration during the working duration of the product. After this, we presented obtained results, on simulated data, and on real-world measured data. Finally, we enhanced the methodology based on tabulated “black box” models which are easier to calibrate and cheaper to process. In conclusion, we reapplied our methodology to a different motion capture sensor, to compile all possible solutions and limits.

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