Le développement de nouveaux convertisseurs d’énergie, plus efficients et plus respectueux de l’environnement, tels que les piles à combustibles, tend à s’accélérer. Leur diffusion à grande échelle suppose cependant des garanties en termes de sécurité et de disponibilité. Une solution possible pour ce faire est de développer des solutions de Prognostics and Health Management (PHM) de ces systèmes, afin de mieux les surveiller, anticiper les défaillances et recommander les actions nécessaires à l’allongement de leur durée de vie. Dans cet esprit, cette thèse porte sur la proposition d’une approche de pronostic dédiée aux piles à combustibles de types PEMFC à l’aide de filtrage particulaire.Le raisonnement s’attache tout d’abord à mettre en place une formalisation du cadre de travail ainsi que des exigences de mise en. Ceci se poursuit par le développement d’un modèle basé sur la physique permettant une estimation d’état de santé et de son évolution temporelle. L’estimation d’état est réalisée grâce à du filtrage particulaire. Différentes variantes de filtres sont considérées sur la base d’une de la littérature et de nouvelles propositions adaptées au PHM sont formulées et comparées à celles existantes. Les estimations d’état de santé fournies par le processus de filtrages ont utilisées pour réaliser des prédictions de l’état de santé futur du système, puis de sa durée devie résiduelle. L’ensemble des propositions est validé sur 4 jeux de données obtenus sur des PEMFC suivant des profils de mission variés. Les résultats montrent de bonnes performances de prédictions et d’estimations de durée de vie résiduelle avant défaillance. / The development of new energy converters, more efficient and environment friendly, such as fuelcells, tends to accelerate. Nevertheless, their large scale diffusion supposes some guaranties in termsof safety and availability. A possible solution to do so is to develop Prognostics and HealthManagement (PHM) on these systems, in order to monitor and anticipate the failures, and torecommend the necessary actions to extend their lifetime. In this spirit, this thesis deals with theproposal of a prognostics approach based on particle filtering dedicated to PEMFCs.The reasoning focuses first on setting a formalization of the working framework and theexpectations. This is pursued by the development of a physic-based modelling enabling a state ofhealth estimation and its evolution in time. The state estimation is made thanks to particle filtering.Different variants of filters are considered on the basis of the literature and new proposals adaptedto PHM are proposed and compared to existing ones. State of health estimates given by the filter areused to predict the future state of the system and its remaining useful life. All the proposals arevalidated on four datasets from PEMFC following different mission profiles. The results show goodperformances for predictions and remaining useful life estimates before failure.

Ce travail de thèse a été initié par Renault, en collaboration avec le Centre de Recherche en Automatique de Nancy (CRAN), dans le but de poser les bases d'une méthodologie générique permettant d'évaluer l'état de santé de moyens de production. Cette méthodologie est issue d’une réflexion conjointe machine - produit en lien avec les exigences industrielles. La méthodologie proposée est basée sur une approche PHM (Prognostics and Health Management) et est structurée en cinq étapes séquentielles. Les deux premières étapes sont développées dans ce travail de thèse et en constituent les contributions scientifiques majeures. La première originalité représente la formalisation des connaissance issues de la relation machine-produit. Cette connaissance est basée sur l'extension de méthodes existantes telle que l’AMDEC et l’HAZOP. La formalisation des concepts de connaissance et de leurs interactions est matérialisée au moyen d'une méta-modélisation basée sur une modélisation UML (Unified Modelling Language). Cette contribution conduit à l'identification de paramètres pertinents à surveiller, depuis le niveau du composant jusqu'au niveau de la machine. Ces paramètres servent ensuite d’entrée au processus d'élaboration du bilan de santé machine, qui représente la deuxième originalité de la thèse. L'élaboration de ces indicateurs de santé est basée sur des méthodes d’agrégation, telle que l'intégrale de Choquet, soulevant la problématique de l'identification des capacités. De cette façon, il est proposé un modèle global d'optimisation de l'identification des capacités multi-niveaux du système à travers l’utilisation d’Algorithmes Génétiques. La faisabilité et l'intérêt d'une telle démarche sont démontrés sur le cas de la machine-outil située à l'usine RENAULT de Cléon / This PhD work has been initiated by Renault, in collaboration with Nancy Research Centre in Automatic Control (CRAN), with the aim to propose the foundation of a generic PHM-based methodology leading to machine health check regarding machine-product joint consideration and facing industrial requirements. The proposed PHM-based methodology is structured in five steps. The two first steps are developed in this PhD work and constitute the major contributions. The first originality represents the formalization of machine-product relationship knowledge based on the extension of well-known functioning/dysfunctioning analysis methods. The formalization is materialized by means of meta-modelling based on UML (Unified Modelling Language). This contribution leads to the identification of relevant parameters to be monitored, from component up to machine level. These parameters serve as a basis of the machine health check elaboration. The second major originality of the thesis aims at the definition of health check elaboration principles from the previously identified monitoring parameters and formalized system knowledge. Elaboration of such health indicators is based on Choquet integral as aggregation method, raising the issue of capacity identification. In this way, it is proposed a global optimization model of capacity identification according to system multi-level, by the use of Genetic Algorithms. Both contributions are developed with the objective to be generic (not only oriented on a specific class of equipment), according to industrial needs. The feasibility and the interests of such approach are shown on the case of machine tool located in RENAULT Cléon Factory

Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) technologies aim at improving the availability, reliability, maintainability, and safety of systems through the development of fault diagnostic and failure prognostic algorithms. In complex engineering systems, such as aircraft, power plants, etc., the prognostic activities have been limited to the component-level, primarily due to the complexity of large-scale engineering systems. However, the output of these prognostic algorithms can be practically useful for the system managers, operators, or maintenance personnel, only if it helps them in making decisions, which are based on system-level parameters. Therefore, there is an emerging need to build health assessment methodologies at the system-level. This research employs techniques from the field of design-of-experiments to build response surface metamodels at the system-level that are built on the foundations provided by component-level damage models.

The development and implementation challenges associated with a proposed load allocation paradigm for fault risk assessment and system health management based on uncertain fault diagnostic and failure prognostic information are investigated. Health management actions are formulated in terms of a value associated with improving system reliability, and a cost associated with inducing deviations from a system's nominal performance. Three simulated case study systems are considered to highlight some of the fundamental challenges of formulating and solving an optimization on the space of available supervisory control actions in the described health management architecture. Repeated simulation studies on the three case-study systems are used to illustrate an empirical approach for tuning the conservatism of health management policies by way of adjusting risk assessment metrics in the proposed health management paradigm. The implementation and testing of a real-world prognostic system is presented to illustrate model development challenges not directly addressed in the analysis of the simulated case study systems. Real-time battery charge depletion prediction for a small unmanned aerial vehicle is considered in the real-world case study. An architecture for offline testing of prognostics and decision making algorithms is explained to facilitate empirical tuning of risk assessment metrics and health management policies, as was demonstrated for the three simulated case study systems.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Providing normal telemetry behavior predictions prior to and post launch will help to stop surprise catastrophic satellite and spacecraft equipment failures. In-orbit spacecraft fail from surprise equipment failures that can result from not having normal telemetry behavior available for comparison with actual behavior catching satellite engineers by surprise. Some surprise equipment failures lead to the total loss of the satellite or spacecraft. Some recovery actions from a surprise equipment failure increase spacecraft risk and involve decisions requiring a level of experience far beyond the responsible engineers.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Providing normal telemetry behavior predictions prior to and post launch will help to stop surprise catastrophic satellite and spacecraft equipment failures. In-orbit spacecraft fail from surprise equipment failures that can result from not having normal telemetry behavior available for comparison with actual behavior catching satellite engineers by surprise. Some surprise equipment failures lead to the total loss of the satellite or spacecraft. Some recovery actions as a consequence of a surprise equipment failure are high risk and involve decisions requiring a level of experience far beyond the responsible engineers.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Current failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.

Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.

Title: Prognosis of the German Bundesliga Objectives: Description of structures and rules of the German Bundesliga. Financial analysis of the German Bundesliga and her prognosis of selected values until the year 2020. Methods used: Financial analysis of absolutes, differentials and relations indicators. Prognostics smallest squares method in Microsoft excel - linear trend function through function forecast. Results: Summary the level of the financial performance based on the financial analysis and prognosis of selected values of the German Bundesliga until the year 2020. Key words: Bundesliga, football, financial source, club, financial analysis, prognostics

We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐ ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms.