Operational modal analysis with non stationnary inputs

Operational modal analysis (OMA) techniques enable the use of in-situ and uncontrolled vibrations to be used to lead modal analysis of structures. In reality operational vibrations are a combination of numerous excitations sources that are much more complex than a random white noise or a harmonic. Numerous OMA techniques exist like SSI, NExT, FDD and BSS. All these methods are based on the fundamental hypothesis that the input or force applied to the structure to be analyzed is a stationary white noise. For some applications this hypothesis is reasonable. However in numerous situations, the analyzed structure is subject to harmonic and transient forces. Numerous methods and research has enabled to develop methods that are robust to such harmonic contributions. To enable OMA during pressure oscillations in solid rocket boosters, the authors propose to consider transient and harmonic inputs no longer as parasites but as the main force applied to the structure that must be analyzed. This is the case during pressure oscillations in rocket boosters. We propose the use of phase analysis adapted to a transient context to conduct operational modal analysis under a harmonic transient input. This time-based novel OMA method will be exposed. The theoretical developments and algorithmic implementations are exposed. First tests have been conducted on laboratory single degree of freedom setup to validate this new OMA technique and are reported here

An aliasing detection algorithm based on suspicious colocalizations of Fourier coefficients

Let u: Ω → R be a discrete gray-level image, where Ω = {0,..., n − 1} 2 is the image domain (we assume that Ω is a square to simplify notations) and u(x) represents the intenhal-00497406

Détection et correction de l aliasing par extension du signal analytique aux images numériques

Cette these porte sur la detection et la correction d'un artefact inherent a toute acquisition numerique, l'aliasing. Nous etudions tout d'abord son effet sur des modeles simples d'images, en particulier les motifs periodiques, qui y sont tres sensibles. Ceci nous amene a definir la relation d'aliasing spectrale, qui caracterise les couples de frequences (ou de zones frequentielles) lies par l'aliasing. Parallelement, en etendant le "signal analytique" aux images, nous obtenons des "parties analytiques", images complexes dont le module est peu impacte par un mauvais echantillonnage. Cette definition permet de localiser spatialement le "domaine d'action" d'une zone spectrale donnee. Remarquant alors qu'une image mal echantillonnee est constituee de beaucoup de couples frequentiels en relation d'aliasing spectrale dont les domaines d'action sont fortement correles, nous pouvons ainsi elaborer deux modeles de detection d'aliasing, selon la methodologie dite a contrario. Les images classiquement utilisees en traitement d'image s'averant peu adaptees, nous construisons une base de test propice a l'etude de l'aliasing afin de valider les algorithmes proposes. Nous utilisons egalement ces algorithmes pour comparer les differents systemes d'acquisition satellitaires existants du point de vue de la creation d'aliasing, grace aux donnees fournies par le CNES. Enfin, partant des resultats des algorithmes de detection, nous proposons une premiere methode de correction de l'aliasing dans les images, et montrons au moyen d'un oracle qu'une hypothese de non-repliement de spectre local pourrait s'averer tres prometteuse.This thesis focuses on the detection and correction of an artifact inherent to all digital acquisition : aliasing. First we study its effects on simples image modelisations. Particularly periodical structures on which aliasing occurs frequently. Then we can define a spectral aliasing link which characterize frequencies (or frequency domains) pairs linked by aliasing relation. In the same time, with an extention of analytic signal, we obtain analytic parts. The modulus of these complex images isn't much impacted by poor sampling. With this definition, we are able to locate on what place of the image occurs a given frequency domain. Bad sampled images contain a lot of frequency pairs in an spectral aliasing link which occurs on the same image domain, we are so able to build two aliasing detection methods with the a contrario framework. The images often used for image processing tests are not suited to the study of aliasing. We build a test database appropriate for the aliasing in order to validate our algorithm. We also use these algorithms to compare some existing earth observation systems, thanks to datas from CNES, with the aliasing point of view. With the results of the detection algorithms, we show with an oracle that a local no-aliasing hypothesis may be very useful for image interpolation.PARIS5-BU Saints-Pères (751062109) / SudocSudocFranceF