This dissertation is ordered into three Parts. Part I is an investigation into identity, indiscernibility and individuality in logic and metaphysics. In Chapter 2, I investigate identity and discernibility in classical first-order logic. My aim will will be to define four different ways in which objects can be discerned from one another, and to relate these definitions: (i) to the idea of symmetry; and (ii) to the idea of individuality. In Chapter 3, the four kinds of discernibility are put to use in defining four rival metaphysical theses about indiscernibility and individuality. Part II sets up a philosophical framework for the work of Part III. In Chapter 4, I give an account of the rational reconstruction of concepts, inspired chiefly by Carnap and Haslanger. I also offer an account of the interpretation of physical theories. In Chapter 5, I turn to the specific problem of finding candidate concepts of particle. I present five desiderata that any putative explication ought to satisfy, in order that the proposed concept is a concept of particle at all. Part III surveys three rival proposals for the concept of particle in quantum mechanics. In Chapter 6, I define factorism and distinguish it from haecceitism. I then propose an amendment to recent work by Saunders, Muller and Seevinck, which seeks to show that factorist particles are all at least weakly discernible. I then present reasons for rejecting factorism. In Chapter 7, I investigate and build on recent heterodox proposals by Ghirardi, Marinatto and Weber about the most natural concept of entanglement, and by Zanardi about the idea of a natural decomposition of an assembly. In Chapter 8, I appraise the first of my two heterodox proposals for the concept of particle, varietism. I define varietism, and then compare its performance against the desiderata laid out in Chapter 5. I argue that, despite its many merits, varietism suffers a fatal ambiguity problem. In Chapter 9, I present the second heterodox proposal: emergentism. I argue that emergentism provides the best concept of particle, but that it is does so imperfectly; so there may be no concept of particle to be had in quantum mechanics. If emergentism is true, then particles are (higher-order) properties of the assembly, itself treated as the basic bearer of properties.

Cette thèse porte sur la conception de méthodes de surveillance de système à partir de données collectées sur des composants de conceptions identiques exploités par plusieurs processus. Nous nous sommes intéressés aux approches de diagnostic sans modèle a priori et plus particulièrement à l'élaboration des modèles de bon fonctionnement des composants à partir des données collectées sur le parc. Nous avons ainsi abordé ce problème comme un problème d'apprentissage multi-tâches qui consiste à élaborer conjointement les modèles de chaque composant, l'hypothèse sous-jacente étant que ces modèles partagent des parties communes. Dans le deuxième chapitre, on considère, dans un premier temps, des modèles linéaires de type multi-entrées/mono-sortie, ayant des structures a priori connues. Dans une première approche, après une phase d'analyse des modèles obtenus par régression linéaire pour les machines prises indépendamment les unes des autres, on identifie leurs parties communes, puis on procède à une nouvelle estimation des coefficients des modèles pour tenir compte des parties communes. Dans une seconde approche, on identifie simultanément les coefficients des modèles ainsi que leurs parties communes. Dans un deuxième temps, on cherche à obtenir directement les relations de redondance existant entre les variables mesurées par l'ACP. On s'affranchit alors des hypothèses sur la connaissance des structures des modèles et on prend en compte la présence d'erreurs sur l'ensemble des variables. Dans un troisième chapitre, une étude de la discernabilité des modèles est réalisée. Il s'agit de déterminer les domaines de variation des variables d'entrée garantissant la discernabilité des sorties des modèles. Ce problème d'inversion ensembliste est résolu soit en utilisant des pavés circonscrits aux différents domaines soit une approximation par pavage de ces domaines. Finalement, une application des approches proposées est réalisée sur des simulateurs d'échangeurs thermiques / This thesis deals with the conception of diagnosis systems using the data collected on identical machines working under different conditions. We are interested in the fault diagnosis method without a priori model and in modelling a fleet of machines using the data collected on all the machines. Hence, the problem can be formulated as a multi-task learning problem where models of the different machines are constructed simultaneously. These models are supposed to share some common parts. In the second chapter, we first consider linear models of type multiple-input/single-output. A first approach consists in analyzing the linear regression models generated using the data of each machine independently from the others in order to identify their common parts. Using this knowledge, new models for the machines are generated. The second approach consists in identifying simultaneously the coefficients of the models and their common parts. Secondly, the redundancy models are searched for using PCA. This way, no hypothesis on the knowledge of the structures of models describing the normal behavior of each machine is needed. In addition, this method allows to take into consideration the errors existing on all the variables since it does not differentiate between input or output variables. In the third chapter, a study on the discernibility of the outputs of the models is realized. The problem consists in identifying the range of variation of the input variables leading to discernible outputs of the models. This problem is solved using either the confined pavements to the different domains or a pavement method. Finally, the multi-task modelling approaches are applied on simulators of heat exchangers