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Dans cette thèse est défendu l'intérêt d'évaluer la santé d'un système/objet industriel multi-composants à travers un bilan de santé multi-niveaux hiérarchiques. Elle a donc pour objet principal de justifier les éléments essentiels du concept de bilan de santé générique qui représente l'état réel d'un système sous la forme d'un vecteur d'indicateurs de différentes natures. Vis-à-vis de ce fondement, la thèse se focalise plus spécifiquement sur les fonctions de détection des anomalies-normalisation et agrégation d'indicateurs pour élaborer un index synthétique représentatif de l'état de santé global pour chaque élément du système. Il est ainsi proposé, une nouvelle approche de détection conditionnelle des anomalies. Cette approche a pour intérêt de quantifier la déviation pour chaque indicateur par rapport à son mode de comportement nominal tout en prenant en compte le contexte dans lequel évolue le système. Une extension à l'exploitation de l'intégrale de Choquet en tant qu'opérateur d'agrégation des indicateurs est aussi proposée. Cette extension concerne, d'une part, un processus d'apprentissage non supervisé des capacités pour le niveau le plus inférieur dans l'abstraction, à savoir celui des composants, et d'autre part, une approche de mise en oeuvre de leur inférence d'un niveau à l'autre. Ces propositions sont appliquées sur un moteur diesel de navire, système essentiel du projet BMCI du pôle MER-PACA dans lequel s'inscrit cette thèse / This work is addressing the health assessment of a multi-component system by means of multi-levels health check-up. Thus scientific Ph. D. objective aims to establish items of a generic health check-up concept. It focuses specifically on the functions of anomaly detection, normalization and aggregation of different indicators to develop a synthetic index representing the overall health status for each element within the system. In that way, it is proposed a new approach for detecting conditional anomalies. This approach has the advantage of quantifying the deviation for each indicator compared to its nominal behavior while taking into account the context in which the system operates. An extension of the Choquet integral used as an operator aggregating indicators is also proposed. This extension regards on the one hand, a process of an unsupervised learning of the capacity coefficients for the lowest level of abstraction, namely components level, and on the other hand, an approach to inference them from one level to another. These contributions are implemented on a ship diesel engine which is the most critical system for the BMCI project of the MER-PACA pole to which this thesis is attached

Dans un marché aéronautique en plein essor marqué par une croissance rapide du parc d’avions utilisées à travers le monde, l’optimisation de la maintenance devient une préoccupation pour les avionneurs. Il s’agit de maximiser la disponibilité des aéronefs tout en réduisant les couts directs d’exploitation sans compromis sur la sécurité des hommes et en respectant les contraintes environnementales. Une stratégie possible pour relever ce challenge est de renforcer la capacité à anticiper les défaillances afin de recourir à des actions préventives le plus juste possible sur les composants les plus critiques comme les roulements à billes. La mise en œuvre de processus de Prognostic Health Management (PHM) prend ainsi une part grandissante et le processus de pronostic est considéré comme l’un des principaux leviers d’action. Son déploiement requiert que le comportement du système étudié puisse être observés. Il peut ainsi bénéficier de l’apport des récentes avancées dans le domaine des microsystèmes autonomes en énergie permettant des mesures intelligentes et un transfert de données d’une manière distribuée, sans aucune aide externe. L’association de ses deux champs de recherche mène naturellement vers le roulement intelligent qui pourrait être la transition d’une maintenance programmée à une maintenance prédictive. Cependant les solutions de PHM autour du roulement et le roulement intelligent, ne restent pas moins le fruit de l’évolution des techniques et technologies de surveillance, de récupération d’énergie et de connectivité. C’est dans ce cadre que s’inscrit ce mémoire de thèse par Validation des Acquis de l’Expérience (VAE). Il s’articule en trois parties principales : motivations du VAE, état de l’art autour du roulement mécatronique et exemple de contributions autour de la récupération d’énergie et la communication sans fil à des fins de surveillance. / The aeronautic market is growing and the aircraft fleet size is becoming bigger. Maintenance optimization is a key focus for liner since objectives are to maximize the aircraft availability and to reduce the direct cost of ownership with no compromise on the flight security and safety with respect to environmental standards. To meet this challenge one possible strategy is to apply a PHM approach using the recent advance in the autonomous embedded microsystem field. This PhD work presents some work done around energy harvesting and wireless sensor to enable a smart bearing able to measure the usage and health data from the component in the purpose of predictive mainteance.

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The emergence of complex and autonomous systems, such as modern aircraft, unmanned aerial vehicles (UAVs) and automated industrial processes is driving the development and implementation of new control technologies aimed at accommodating incipient failures to maintain system operation during an emergency. The motivation for this research began in the area of avionics and flight control systems for the purpose to improve aircraft safety. A prognostics health management (PHM) based fault-tolerant control architecture can increase safety and reliability by detecting and accommodating impending failures thereby minimizing the occurrence of unexpected, costly and possibly life-threatening mission failures; reduce unnecessary maintenance actions; and extend system availability / reliability. Recent developments in failure prognosis and fault tolerant control (FTC) provide a basis for a prognosis based reconfigurable control framework. Key work in this area considers: (1) long-term lifetime predictions as a design constraint using optimal control; (2) the use of model predictive control to retrofit existing controllers with real-time fault detection and diagnosis routines; (3) hybrid hierarchical approaches to FTC taking advantage of control reconfiguration at multiple levels, or layers, enabling the possibility of set-point reconfiguration, system restructuring and path / mission re-planning. Combining these control elements in a hierarchical structure allows for the development of a comprehensive framework for prognosis based FTC. First, the PHM-based reconfigurable controls framework presented in this thesis is given as one approach to a much larger hierarchical control scheme. This begins with a brief overview of a much broader three-tier hierarchical control architecture defined as having three layers: supervisory, intermediate, and low-level. The supervisory layer manages high-level objectives. The intermediate layer redistributes component loads among multiple sub-systems. The low-level layer reconfigures the set-points used by the local production controller thereby trading-off system performance for an increase in remaining useful life (RUL). Next, a low-level reconfigurable controller is defined as a time-varying multi-objective criterion function and appropriate constraints to determine optimal set-point reconfiguration. A set of necessary conditions are established to ensure the stability and boundedness of the composite system. In addition, the error bounds corresponding to long-term state-space prediction are examined. From these error bounds, the point estimate and corresponding uncertainty boundaries for the RUL estimate can be obtained. Also, the computational efficiency of the controller is examined by using the number of average floating point operations per iteration as a standard metric of comparison. Finally, results are obtained for an avionics grade triplex-redundant electro-mechanical actuator with a specific fault mode; insulation breakdown between winding turns in a brushless DC motor is used as a test case for the fault-mode. A prognostic model is developed relating motor operating conditions to RUL. Standard metrics for determining the feasibility of RUL reconfiguration are defined and used to study the performance of the reconfigured system; more specifically, the effects of the prediction horizon, model uncertainty, operating conditions and load disturbance on the RUL during reconfiguration are simulated using MATLAB and Simulink. Contributions of this work include defining a control architecture, proving stability and boundedness, deriving the control algorithm and demonstrating feasibility with an example.

I have dealt with fleet management analysis in my thesis. The base of this analysis is monitoring development trends in this area. Development trends are especially mobile phones, software equipment, satellite systems, digital tachographs, fulfilment conditions of emission standards EURO 4 and EURO 5 (technologies EGR and SCR), quality and environment management and last but not least problems in fuel consumption management.

擔保房貸憑證(CMOs)是衍生自不動產抵押證券(MBS)的證券化商品，透過分券的特殊設計，藉此降低MBS商品的提前清償風險，也增加市場投資人的選擇彈性。但特殊的設計同時也提高了評價上的困難度，由於此產品的提前清償狀況會依分券性質與標的抵押貸款借款人的償還情形而有所不同，為了準確掌握CMOs商品的現金流量，必須採用較為充分的貸款資料與提前清償模型來推估提前清償機率。本文採用競爭風險模型來做為提前清償模型，透過較多變數的選擇方式，模擬出較為精確的提前清償機率路徑。 此外，BGM利率模型是透過將利率間斷化，捕捉市場上可觀察到的利率，同時也可提供在不同時點下的即期利率，可藉此推算出不同時點下的分券價格，故採用BGM來做為本文的利率模型。

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classication for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to nd a good trade-o between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data, is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines , (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines,(c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

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.

Dans de nombreux secteurs industriels, la miniaturisation des systèmes est devenue une nécessité afin de réduire l’espace occupé, le poids, les prix et la consommation d’énergie et de matière. Pour ce faire, les industriels utilisent les Micro-Electro-Mechanical Systems (MEMS). En revanche, les MEMS présentent plusieurs problèmes de fiabilité dus à leurs nombreux mécanismes de défaillance qui ont un impact sur la disponibilité des systèmes dans lesquels ils sont utilisés. Il est alors important de surveiller ces microsystèmes, d’anticiper leurs défaillances et de recommander les actions nécessaires afin d’allonger leur durée de vie. Une solution efficace pour ce faire est de développer le Prognostics & Health Management (PHM) pour les MEMS. Dans cet esprit, la thèse porte sur le pronostic et l’étude de l’état de santé de MEMS et la prise de décision post-pronostic dans les systèmes contenant ces microsystèmes. L’objectif est de rendre un système à base de MEMS distribué intelligent en intégrant des modules d’évaluation et de prédiction de l’état de santé du système ainsi que des capacités d’auto-adaptation dépendant des missions que le système doit accomplir. Dans un premier temps, une approche de pronostic hybride pour les MEMS basée sur le filtrage particulaire est proposée. Dans un second temps, et afin de mieux utiliser les résultats de cette approche, une stratégie de décision post-pronostic dans les systèmes distribués à base de MEMS est introduite. Un simulateur distribué a été développé pour simuler la décision post-pronostic. La performance de l’approche de pronostic et de la stratégie de décision post-pronostic est validée sur une application réelle, à savoir un convoyeur modulaire à base de MEMS distribués. Un cycle complet de PHM est ainsi développé : de l’acquisition des données à la prise de décision. / In many industrial sectors, system miniaturization becomes mandatory, allowing reducing occupied space, weight, price, power and material consumption. For this, manufacturers use Micro-Electro- Mechanical Sytems (MEMS). However, MEMS devices have several reliability issues due to their numerous failure mechanisms, which have an impact on the availability of systems where they are utilized. Therefore, it is important to monitor these micro-systems, to anticipate their failures and to perform appropriate actions to maximize their lifespan. One possible solution is to develop the Prognostics & Health Management (PHM) for MEMS. The thesis deals then with the prognostics and the study of MEMS health state and the post-prognostics decision making in systems containing these micro-systems. The aim is to make a MEMS-based system distributed and intelligent by integrating modules of health state assessment and prediction and capacities of self-adaptability dependent of the tasks performed by the system. Firstly, a hybrid prognostics approach for MEMS based on the particle filtering is proposed. Secondly, and to better use the results of this approach, a post-prognostics decision strategy in MEMS-based distributed systems is introduced. This strategy is based on a distributed decision algorithm. The performance of the prognostics approach and the post-prognostics strategy is validated on a real application consisting of a modular conveyor based on distributed MEMS. A complete PHM cycle is thus performed: from data acquisition to decision making.