The purpose of this research was a proof of concept using a fan motor stator as transducer to monitor motor rotor and attached axial fan for mechanical motion. The proof was to determine whether bearing faults and fan imbalances could be detected in vane-axial fans using Motor Electrical Signature Analysis (MESA). The data was statistically analyzed to determine if the MESA systems could distinguish between baseline conditions and discrete fault frequencies for the three test conditions: bearing inner race defect, bearing outer race defect, and fan imbalance. The statistical conclusions for these proofs of concept were that MESA could identify all three faulted conditions.

The union of a set of materials by way of Resistance Spot Welding is designed so that once fused together, a substantial amount of intentional, external force must be applied to separate the contents. Therefore, Resistance Spot Welding is often the preferred fusion method in high-volume manufacturing processes. The result of Resistance Spot Welding however is the formation of a weld nugget which is not visible to the naked eye. Destructive and/or ultrasonic methods applied off-line must be used to determine the quality of each weld; both inefficient and expensive processes. The following research analyzes the data fed back during resistance spot weld sequences in-line and establishes a correlation between emitted characteristics and the final quality of a spot weld. The two characteristics researched to segregate weld quality are: the electrical sin wave signature and the acoustic sin wave signature produced during the welding sequence. Both features were discovered to have a direct correlation to the final quality of a weld once cured. By measuring and comparing these characteristics at the source, an opportunity is presented to decrease time and potential defects by confirming the quality of each weld in-process and at the source.

Face aux enjeux climatiques actuels, le secteur bâtiment est encouragé à réduire sa consommation énergétique tout en préservant le confort des occupants. C’est dans ce contexte que s’inscrit le projet ANR PRECCISION qui vise au développement d’outils et de méthodes pour la gestion énergétique optimisée des bâtiments qui nécessitent l’utilisation de modèles thermiques dynamiques. Les travaux de thèse, effectués entre le G2Elab et le G-SCOP, se sont focalisés sur les problématiques liées à l’estimation paramétrique de ces modèles. En effet, les incertitudes liées aux phénomènes mal maîtrisés et la nature des modèles rendent le calibrage des paramètres des modèles délicat. Cette procédure complexe n’est à ce jour pas systématisable : les modèles auto-regressifs ont une faible capacité d'extrapolation car leur structure est inadaptée, tandis que les modèles physiques sont non-linéaires par rapport à de nombreux paramètres : les estimations conduisent à des optimums locaux fortement dépendant de l'initialisation. Pour lever ce verrou, plusieurs approches ont été explorées à partir de modèles physiques adaptés pour lesquels des études sur l’identifiabilité ont été menées sur une plateforme expérimentale : PREDIS MHI. Différentes stratégies d'optimisation sont alors proposées visant à déterminer les paramètres qui peuvent être recalés. La première approche repose sur une analyse a priori de la dispersion paramétrique, la seconde repose sur une procédure de méta-optimisation qui détermine dynamiquement, au fur et à mesure d'une séquence d'optimisations, les paramètres à recaler. Les résultats sont analysés et comparés à diverses approches (modèles universels, identification « naïve » de tous les paramètres d’un modèle physique, algorithme génétique, …) à travers différents cas d'application. / In order to tackle the actual climate issues, the building field is encouraged to reduce his energetic consumption without changing the occupant’s comfort. In this context, the aim of the ANR PRECCISION project is to develop tools and methods for energetic management of the buildings which needs the use of dynamical thermal models. The PHD works, realise between the G2Elab and the G-SCOP, was focused on models parametric estimation issues. Indeed, uncertainties due to unknown phenomena and the nature of models lead to difficulties for the calibration of the models. Nowadays, this complex procedure is still not automatable: auto-regressive models have a low capacity to extrapolate because of their inadequate structure, whereas the physical models are non-linear regarding many parameters: estimations lead towards local optimums which highly depend on the initial point. In order to eliminate these constraints, several approaches have been explored with physical models adapted for which identifiability studies have been reached on an experimental platform: PREDIS MHI. Different optimisation strategies will be proposed in order to determine the parameters which can be estimated. The first approach uses an analyse a priori of the parametric dispersion, the second one use a meta optimisation which dynamicaly determined as the optimisation sequence, the parameters which can be readjusted. The results are analysed and compared to several approaches (universal models, “simple” identification of all the parameters of a physical model, genetic algorithm …) in different application cases.