Thèses sur le sujet « Pronostic de durée de vie résiduelle »
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Delmas, Adrien. « Contribution à l'estimation de la durée de vie résiduelle des systèmes en présence d'incertitudes ». Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2476/document.
Texte intégralPredictive maintenance strategies can help reduce the ever-growing maintenance costs, but their implementation represents a major challenge. Indeed, it requires to evaluate the health state of the component of the system and to prognosticate the occurrence of a future failure. This second step consists in estimating the remaining useful life (RUL) of the components, in Other words, the time they will continue functioning properly. This RUL estimation holds a high stake because the precision and accuracy of the results will influence the relevance and effectiveness of the maintenance operations. Many methods have been developed to prognosticate the remaining useful life of a component. Each one has its own particularities, advantages and drawbacks. The present work proposes a general methodology for component RUL estimation. The objective i to develop a method that can be applied to many different cases and situations and does not require big modifications. Moreover, several types of uncertainties are being dealt With in order to improve the accuracy of the prognostic. The proposed methodology can help in the maintenance decision making process. Indeed, it is possible to select the optimal moment for a required intervention thanks to the estimated RUL. Furthermore, dealing With the uncertainties provides additional confidence into the prognostic results
Shahin, Kamrul. « Modèle graphique probabiliste appliqué au diagnostic de l'état de santé des systèmes, au pronostic et à l'estimation de la durée de vie résiduelle ». Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0129.
Texte intégralThis thesis contributes to prognosis and health management for assessing health condition of complex systems. In the context of operational management and operational safety of systems, we propose to investigate how Dynamic Probabilistic Graphical Modelling (DPGM) can be used to diagnose the current health state of systems, prognostic the future health state, and the evolution of degradation, as well as estimate its remaining useful life based on its operating conditions. System degradation is generally unknown and requires shutting down the system to be observed. However, this is difficult or even impossible during system operation. Though, a set of observable quantities on a system or component can characterise the level of degradation and help to estimate the remaining useful life of components and systems. The DPGM provides an approach suitable for modelling the evolution of the health state of systems and components. The aim of this thesis is to transpose and capitalize on the experience of these previous works in a prognostic context on the basis of a more efficient DPGM taking into account the available knowledge on the system. We extend the classical HMM family models to the IOHMM to allow the time propagation of uncertainty to address prognostic problems. This research includes the extension of learning and inference algorithms. Variants of the HMM model are proposed to incorporate the operating environment into the prognosis. The aim of this thesis is to contribute to solving the following scientific locks: - Considering the state of health whatever the complexity of the system by a stochastic model and learning the model parameters from the available measurements on the system. - Establish a diagnosis of the state of health of the system and the prognosis of its evolution by integrating several operational conditions. - Estimate the remaining useful life of components and structured systems with series and parallel components. This is a major challenge because the prognosis of the degradation of system components makes it possible to define strategies for either control or maintenance in relation to the residual life of the system. This allows the reduction of the probability of occurrence of a shutdown due to a system malfunction either by adjusting the degradation speed to fit in with a preventive maintenance plan or by proactively planning maintenance interventions
Duchene, Pierre. « Caractérisation non destructive des matériaux composites en fatigue : diagnostic de l’état de santé et pronostic de la durée de vie résiduelle par réseaux de neurones ». Thesis, Ecole nationale supérieure Mines-Télécom Lille Douai, 2018. http://www.theses.fr/2018MTLD0008.
Texte intégralThis research work consists in a new approach for non-destructive characterisation of damage in composite materials (carbon/epoxy) subjected to fatigue during self-heating tests (increasing load blocks). This approach is based on the use of several non-destructive techniques applied in-situ, in real time or delayed, whose analysis is either redundant or complementary. Six techniques were used (acoustic emission, infrared thermography, digital image correlation, acousto-ultrasound, C-scan ultrasound and lamb waves) and their post-processed results were merged using algorithms based on neural networks. The results obtained made it possible to assess and locate the damage of the material and to estimate its residual life. In doing so, several scientific advances have been obtained by, for example, carrying out a 2D localization of acoustic events using only two sensors with millimetric precision, or the development of a new pictorial acousto-ultrasonic technique allowing an control of the state of material damage at free stress conditions, ... and finally, the prognosis of the residual lifetime of the material based on a data fusion by neural networks
Aggab, Toufik. « Pronostic des systèmes complexes par l’utilisation conjointe de modèle de Markov caché et d’observateur ». Thesis, Orléans, 2016. http://www.theses.fr/2016ORLE2051/document.
Texte intégralThe research presented in this thesis deals of diagnosis and prognosis of complex systems. It presents two approaches that generate useful indicators for optimizing maintenance strategies. Specifically, these approaches are used to assess the level of degradation and estimate the Remaining Useful Life of the system. The aim of these approaches is to overcome for the lack of degradation indicators. The developments are based on observers, Hidden Markov Model formalism, statistical inference methods and learning-based methods in order to characterize and predict the system operating modes. To illustrate the proposed failure diagnosis/prognosis approaches, a simulated tank level control system, an induction motor and a Li-Ion battery were used
Ammour, Rabah. « Contribution au diagnostic et pronostic des systèmes à évènements discrets temporisés par réseaux de Petri stochastiques ». Thesis, Normandie, 2017. http://www.theses.fr/2017NORMLH21/document.
Texte intégralDue to the increasing complexity of systems and to the limitation of sensors number, developing monitoring methods is a main issue. This PhD thesis deals with the fault diagnosis and prognosis of timed Discrete Event Systems (DES). For that purpose, partially observed stochastic Petri nets are used to model the system. The model represents both the nominal and faulty behaviors of the system and characterizes the uncertainty on the occurrence of events as random variables with exponential distributions. It also considers partial measurements of both markings and events to represent the sensors of the system. Our main contribution is to exploit the timed information, namely the dates of the measurements for the fault diagnosis and prognosis of DES. From the proposed model and collected measurements, the behaviors of the system that are consistent with those measurements are obtained. Based on the event dates, our approach consists in evaluating the probabilities of the consistent behaviors. The probability of faults occurrences is obtained as a consequence. When a fault is detected, a method to estimate its occurrence date is proposed. From the probability of the consistent trajectories, a state estimation is deduced. The future possible behaviors of the system, from the current state, are considered in order to achieve fault prediction. This prognosis result is extended to estimate the remaining useful life as a time interval. Finally, a case study representing a sorting system is proposed to show the applicability of the developed methods
Hoang, Anh. « Pronostic de la performance d’Efficacité Energétique pour la prise de décision en maintenance dans les systèmes industriels ». Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0086/document.
Texte intégralAmong sustainability consideration, energy is today the key for economic growth in industrial systems. Energy resources are however limited and becomes more and more expensive. The energy optimization of manufacturing systems must therefore be considered as a major challenge to be compliant with environmental impact and management of energy resources. This should be reflected primarily by using energy efficiency (EE) as main key lever to deploy sustainability to plants, i.e. reduce the amount of energy required to provide products and services. With regards to this EE context, the aim of this thesis is to investigate the problem of considering energy efficiency and its prediction as a new indicator in maintenance decision-making. In that way, we develop first a concept of energy efficiency, called EEI (energy efficiency indicator), applicable to the different levels of abstraction of an industrial system. Then, we propose a generic formulation to evaluate the EEI (and its evolution) taking into account static and dynamic factors of influence. The temporal evolution of this indicator with respect to the degradation of the system is addressed in a predictive maintenance objective. It leads to found an energy efficiency performance concept called REEL (remaining energy-efficient lifetime), representing the residual energy lifetime. To predict the potential evolution of the IEE to calculate REEL, a generic approach based on existing predictive approaches is also developed. Next, we investigate the use of EE in CBM maintenance decision-making. Finally, all these contributions are validated on the TELMA platform
Hoang, Anh. « Pronostic de la performance d’Efficacité Energétique pour la prise de décision en maintenance dans les systèmes industriels ». Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0086.
Texte intégralAmong sustainability consideration, energy is today the key for economic growth in industrial systems. Energy resources are however limited and becomes more and more expensive. The energy optimization of manufacturing systems must therefore be considered as a major challenge to be compliant with environmental impact and management of energy resources. This should be reflected primarily by using energy efficiency (EE) as main key lever to deploy sustainability to plants, i.e. reduce the amount of energy required to provide products and services. With regards to this EE context, the aim of this thesis is to investigate the problem of considering energy efficiency and its prediction as a new indicator in maintenance decision-making. In that way, we develop first a concept of energy efficiency, called EEI (energy efficiency indicator), applicable to the different levels of abstraction of an industrial system. Then, we propose a generic formulation to evaluate the EEI (and its evolution) taking into account static and dynamic factors of influence. The temporal evolution of this indicator with respect to the degradation of the system is addressed in a predictive maintenance objective. It leads to found an energy efficiency performance concept called REEL (remaining energy-efficient lifetime), representing the residual energy lifetime. To predict the potential evolution of the IEE to calculate REEL, a generic approach based on existing predictive approaches is also developed. Next, we investigate the use of EE in CBM maintenance decision-making. Finally, all these contributions are validated on the TELMA platform
Abou, Jaoude Abdo. « Advanced analytical model for the prognostic of industrial systems subject to fatigue ». Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4331/document.
Texte intégralThe high availability of technological systems like aerospace, defense, petro-chemistry and automobile, is an important goal of earlier recent developments in system design technology knowing that the expensive failure can generally occur suddenly. To make the classical strategies of maintenance more efficient and to take into account the evolving product state and environment, a new analytic prognostic model is developed as a complement of existent maintenance strategies. This new model is applied to mechanical systems that are subject to fatigue failure under repetitive cyclic loading. Knowing that, the fatigue effects will initiate micro-cracks that can propagate suddenly and lead to failure. This model is based on existing damage laws in fracture mechanics, such as the crack propagation law of Paris-Erdogan beside the damage accumulation law of Palmgren-Miner. From a predefined threshold of degradation DC, the Remaining Useful Lifetime (RUL) is estimated by this prognostic model. Damages can be assumed to be accumulated linearly (Palmgren-Miner's law) and also nonlinearly to take into consideration the more complex behavior of loading and materials. The degradation model developed in this work is based on the accumulation of a damage measurement D after each loading cycle. When this measure reaches the predefined threshold DC, the system is considered in wear out state. Furthermore, the stochastic influence is included to make the model more accurate and realistic
Jha, Mayank Shekhar. « Diagnostic et Pronostic de Systèmes Dynamiques Incertains dans un contexte Bond Graph ». Thesis, Ecole centrale de Lille, 2015. http://www.theses.fr/2015ECLI0027/document.
Texte intégralThis thesis develops the approaches for diagnostics and prognostics of uncertain dynamic systems in Bond Graph (BG) modeling framework. Firstly, properties of Interval Arithmetic (IA) and BG in Linear Fractional Transformation, are integrated for representation of parametric and measurement uncertainties on an uncertain BG model. Robust fault detection methodology is developed by utilizing the rules of IA for the generation of adaptive interval valued thresholds over the nominal residuals. The method is validated in real time on an uncertain and highly complex steam generator system.Secondly, a novel hybrid prognostic methodology is developed using BG derived Analytical Redundancy Relationships and Particle Filtering algorithms. Estimations of the current state of health of a system parameter and the associated hidden parameters are achieved in probabilistic terms. Prediction of the Remaining Useful Life (RUL) of the system parameter is also achieved in probabilistic terms. The associated uncertainties arising out of noisy measurements, environmental conditions etc. are effectively managed to produce a reliable prediction of RUL with suitable confidence bounds. The method is validated in real time on an uncertain mechatronic system.Thirdly, the prognostic methodology is validated and implemented on the electrical electro-chemical subsystem of an industrial Proton Exchange Membrane Fuel Cell. A BG of the latter is utilized which is suited for diagnostics and prognostics. The hybrid prognostic methodology is validated, involving real degradation data sets
Hervé, de Beaulieu Martin. « Identification et pronostics de l’état de santé des systèmes non linéaires par apprentissage profond. Application à la maintenance prévisionnelle des avions d’affaires ». Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0227.
Texte intégralState-of-Health prognostics is a major challenge in the predictive maintenance domain, and has been the subject of numerous studies in recent years, with particular emphasis on the use of Artificial Intelligence (AI) to improve prediction performance. However, few realistic approaches have been proposed so far that take into account the real industrial constraints, and in particular the lack of data under degradation. The aim of this PhD work is to propose an AI-based prognostics approach as realistic as possible, addressing in particular the problem of the absence of degradation data, and leveraging the available a priori knowledge. A global prognostics approach in the absence of measured degradation data is proposed. It is divided into three main stages. First of all, a hybrid data augmentation phase based on system identification coupled with the injection of a physics-based degradation model is used to generate both nominal data and degradation data. Next, an unsupervised Health Index (HI) extraction method, using the reconstruction error of an autoencoder, is used to obtain a HI from the sensor data collected on the system. Finally, a long-term HI prediction process leads to Remaining Useful Life (RUL) predictions. Some stages are first validated on an academic dataset (C-MAPSS), then the overall method is applied to a real industrial case thanks to a partnership with Dassault Aviation. The research conducted highlights the need for approaches that are realistic from an industrial point of view, taking account of real-life constraints, and the results obtained open up new opportunities for the practical use of AI in predictive maintenance
Dias, Longhitano Pedro. « Maintenance prescriptive pour la gestion optimale de systèmes soumis à dégradation : application à la prescription conjointe de modes d’utilisation et d’actions de maintenance pour des véhicules industriels ». Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALT010.
Texte intégralWith digitalization, the internet of things and the popularization of data-centric services, different economy sectors have gone through significant changes in their business model. For the automotive sector in particular, those changes relate to moving towards more service-oriented offers. Selling a vehicle is no longer the last contact with the client, as aftermarket services are responsible for a significant part of vehicle manufacturer's revenue. With that in mind, the main concern of this research project is to lay down the foundations for enabling future services for heavy vehicles.This work stresses the importance of maintenance optimization as well as its relationship with vehicle usage. In the past, maintenance was limited to corrective replacements of faulty parts resulting in long idle periods. This impacted the activity of the transporter, as well as its repair costs. The introduction of monitoring systems along with the current communication techniques allow the development of new optimization methods in which, not only replacement dates are determined optimally but also, vehicle usage is changed accordingly, ensuring cost optimization, and continuously extending trucks remaining useful life. With today's technology, those optimization methods could be turned into services that help clients defining replacement dates, manage logistics to minimize degradation levels of the fleet, or even change vehicle software parameters to minimize the long-term costs.This approach to maintenance, in which life is extend through a myriad of different actions encompassing several aspects of vehicle usage, is often referred to as prescriptive maintenance. Although prescriptive maintenance was from the beginning an important element of this work, its definition is rather controversial in the literature. As a consequence, a conceptual effort is done in this document in order to clarify the scope of this kind of maintenance paradigm. With more precise definitions and a clear scope, prescriptive maintenance is applied in the context of heavy vehicles.Prescriptive maintenance applications for heavy vehicles can be seen as original optimization problems in the realm of transportation science. Throughout the investigation of such methods, significant scientific contributions were made. First and foremost, prescriptive maintenance requires models that can realistically connect degradation and vehicle usage. Such models are hardly adapted for decision-making problems and require adaptation. In this document, such models are studied in detail.Solving those aforementioned optimization problems is a non-trivial task. All of the aforementioned formulations required significant computational effort to be solved exactly. As such, another dimension of this work contribution is the development of algorithms suitable for solving them. Classical methods are adapted taking advantage of particular properties of the models used and heuristics capable of closing the optimization gap in a reasonable time are developed
Khoury, Elias. « Modélisation de la durée de vie résiduelle et maintenance prédictive : application à des véhicules industriels ». Troyes, 2012. http://www.theses.fr/2012TROY0027.
Texte intégralMaintenance has become in many fields such as the automotive field, a very important aspect due mostly to its economic dimension. In this context, we are interested in improving maintenance decision making in order to reduce its costs mainly. We focus specifically on the predictive maintenance approach using the residual useful lifetime (RUL) as a tool for decision support. The RUL integrates information about the state of a system and its environment in the past, present and future (prediction). At first, we consider degradation based failure models. We study and develop several models that can describe different behaviours of degradation and failure mechanisms. In particular we consider a case study on engine oil. For these different models, we propose methods to estimate the distribution of the RUL conditionally to the state of the system and its environment. Subsequently, we propose predictive maintenance strategies in several configurations and we show how the RUL can be used in decision making. The conducted studies show the benefit of using the RUL and allow us to quantify the resulting gain depending on the considered case and the way the RUL is used
Silva, Sanchez Rosa Elvira. « Contribution au pronostic de durée de vie des systèmes piles à combustible PEMFC ». Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2005/document.
Texte intégralThis thesis work aims to provide solutions for the limited lifetime of Proton Exchange Membrane Fuel Cell Systems (PEM-FCS) based on two complementary disciplines:A first approach consists in increasing the lifetime of the PEM-FCS by designing and implementing a Prognostics & Health Management (PHM) architecture. The PEM-FCS are essentially multi-physical systems (electrical, fluid, electrochemical, thermal, mechanical, etc.) and multi-scale (time and space), thus its behaviors are hardly understandable. The nonlinear nature of phenomena, the reversibility or not of degradations and the interactions between components makes it quite difficult to have a failure modeling stage. Moreover, the lack of homogeneity (actual) in the manufacturing process makes it difficult for statistical characterization of their behavior. The deployment of a PHM solution would indeed anticipate and avoid failures, assess the state of health, estimate the Remaining Useful Lifetime (RUL) of the system and finally consider control actions (control and/or maintenance) to ensure operation continuity.A second approach proposes to use a passive hybridization of the PEMFC with Ultra Capacitors (UC) to operate the fuel cell closer to its optimum operating conditions and thereby minimize the impact of aging. The UC appear as an additional source to the PEMFC due to their high power density, their capacity to charge/discharge rapidly, their reversibility and their long life. If we take the example of fuel cell hybrid electrical vehicles, the association between a PEMFC and UC can be performed using a hybrid of active or passive type system. The overall behavior of the system depends on both, the choice of the architecture and the positioning of these elements in connection with the electric charge. Today, research in this area focuses mainly on energy management between the sources and embedded storage and the definition and optimization of a power electronic interface designated to adjust the flow of energy between them. However, the presence of power converters increases the source of faults and failures (failure of the switches of the power converter and the impact of high frequency current oscillations on the aging of the PEMFC), and also increases the energy losses of the entire system (even if the performance of the power converter is high, it nevertheless degrades the overall system)
Huang, Fei. « Contributions à l'élaboration des modèles à partir de données pour l'estimation de la durée de vie résiduelle des roulements ». Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0019.
Texte intégralRemaining useful life (RUL) estimation for bearings degradation monitoring is an important metric for decision making in condition based maintenance of rotating mechanics. RUL estimation involves generally two steps: degradation indicator extraction and model identification. Common vibration signal based features for bearings degradation monitoring are sensible on the last stage of the degradation process. In this thesis, we propose new bearing degradation monitoring indicators that are monotonic and incorporate historical degradation information. To overcome the drawback of a small size training datasets for model identification, we elaborated a mixture distribution analysis based fuzzy model identification method for RUL estimation. Furthermore, we proposed a method to tune the parameters of the fuzzy models for bearings RUL estimation when new knowledge becomes available. The aim is to improve the accuracy of the RUL estimation through a knowledge accumulation process
Esseghir, Moez. « Déploiement et stratégies d'optimisation de la durée de vie dans les réseaux de capteurs sans fil ». Paris 6, 2007. http://www.theses.fr/2007PA066602.
Texte intégralKhelif, Racha. « Estimation du RUL par des approches basées sur l'expérience : de la donnée vers la connaissance ». Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2019/document.
Texte intégralOur thesis work is concerned with the development of experience based approachesfor criticalcomponent prognostics and Remaining Useful Life (RUL) estimation. This choice allows us to avoidthe problematic issue of setting a failure threshold.Our work was based on Case Based Reasoning (CBR) to track the health status of a new componentand predict its RUL. An Instance Based Learning (IBL) approach was first developed offering twoexperience formalizations. The first is a supervised method that takes into account the status of thecomponent and produces health indicators. The second is an unsupervised method that fuses thesensory data into degradation trajectories.The approach was then evolved by integrating knowledge. Knowledge is extracted from the sensorydata and is of two types: temporal that completes the modeling of instances and frequential that,along with the similarity measure refine the retrieval phase. The latter is based on two similaritymeasures: a weighted one between fixed parallel windows and a weighted similarity with temporalprojection through sliding windows which allow actual health status identification.Another data-driven technique was tested. This one is developed from features extracted from theexperiences that can be either mono or multi-dimensional. These features are modeled by a SupportVector Regression (SVR) algorithm. The developed approaches were assessed on two types ofcritical components: turbofans and ”Li-ion” batteries. The obtained results are interesting but theydepend on the type of the treated data
Le, Son Khanh. « Modélisation probabiliste du pronostic : application à un cas d'étude et à la prise de décision en maintenance ». Troyes, 2012. http://www.theses.fr/2012TROY0035.
Texte intégralRemaining useful life (RUL) estimation is a major scientific challenge and a principal topic in the scientific community which takes an interest to prognosis problems. The use of tools and methods collected under the terms of prognostic is widely developed in many domains as aerospace industry, electronics, medicine, etc. The common underlying problem is the implementation of models which can take into account on-line the data histories of system and its environment, the diagnosis on its current state and possibly the future operational conditions for predicting the residual lifetime. In this context, the principal problem of our works is the use of probabilistic approaches (type of non-stationary stochastic process) to construct the innovatory prognostic models from a degradation indicator of system and to use the residual lifetime prediction for maintenance implementation. The advantage of these models is to have the regularity proprieties which make easy the probability calculation and RUL estimation. In order to test the performances of our models, a comparative study is carried out on the data provided by the 2008 IEEE Prognostic and Health Management (PHM)
Skima, Haithem. « Pronostic et algorithmes distribués de décision post-pronostic dans les systèmes à base de MEMS ». Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2040/document.
Texte intégralIn 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
El, Hayek Antoine. « Analyse de vieillissement, estimation de la durée de vie et méthode de surveillance de l’état de santé des condensateurs électrolytiques ». Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE1037.
Texte intégralWith the emergence of new technologies, we are witnessing the development of techniques to improve dependability and in particular the maintainability of static energy converters and their components. In these conversion systems, the electrolytic capacitors, ensuring a stable DC network, represent an important element in the AC / DC and / or DC / AC electrical energy conversion chain. In operation, they are subject to electrical and environmental constraints (ambient temperature, current ripple, applied voltage, humidity, vibrations, etc.). These capacitors undergo redox reactions, which consume and evaporate electrolyte. The lifetime of the capacitor is thus affected. Therefore, it is interesting to estimate the state of health of these components in order to be able to schedule maintenance operations. It is therefore useful to develop tools for applying conditional monitoring. In the context of this work, the objective is to propose a system for monitoring the state of health of the electrolytic capacitors. We have therefore developed evolution models of their aging indicators which are the variations of the equivalent series resistance ESR and equivalent capacity C. The prediction algorithm is based on the evolution of these indicators to estimate the state remaining life of the capacitor module. The real-time monitoring system developed does not include additional sensors to those already existing in the energy converters considered and does not require prior accelerated aging tests. In this manuscript, we first detail the experimental procedure for accelerated aging, the different stages of characterization, the aging process and the associated results. We propose a simple and effective method to identify ESR and C aging indicators in real time. The proposed algorithm is based on time scaling and on a reference frame of temperature and voltage constraints. Subsequently, the results of the simulation of the surveillance system and the forecast of the selected state of health are presented. Experimental tests have been carried out on capacitors integrated into an industrial speed controller with a power of 15 kW. The algorithms implemented and their respective implementation constraints, for a real-time application, are detailed
Nguyen, Danh Ngoc. « Contribution aux approches probabilistes pour le pronostic et la maintenance des systèmes contrôlés ». Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0010/document.
Texte intégralThe automatic control systems play an important role in the development of civilization and modern technology. The loss of effectiveness of the actuator acting on the system is harmful in the sense that it modifies the behavior of the system compared to that desired. This thesis is a contribution to the prognosis of the remaining useful life (RUL) and the maintenance of closed loop systems with actuators subjected to degradation. In the first contribution, a modeling framework with piecewise deterministic Markov process is considered in order to model the overall behavior of the system. In this context, the behavior of the system is represented by deterministic trajectories that are intersected by random size jumps occurring at random times and modeling the discrete degradation phenomenon of the actuator. The second contribution is a prognosis method of the system RUL which consists of two steps: the estimation of the probability distribution of the system state at the prognostic instant by particle filtering and the computation of the RUL which requires the estimation of the system reliability starting from the prognostic instant. The third contribution is the proposal of a parametric maintenance policy which dynamically take into account the available information on the state and on the current environment of the system and under the constraint of opportunity dates
Bressel, Mathieu. « Modélisation raphique pour le pronostic robuste de pile à combustible à membrane échangeuse de proton ». Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10119/document.
Texte intégralThe fuel cell (FC) is at present the alternative solution to the fossil fuels the most promising. It is however advisable to improve its reliability. This requires the implementation of algorithms capable of estimating in real time the state of health and forecasting its remaining useful life (prognostics). The methods of prognostics based on a physical model offer precise results once they do not requiring either learning or expertise of the operator. However, the problem for a FC system lies in the coupling of several physical phenomena, the uncertainty of the parameters of the model and the low instrumentation of the FC stack.Thus, we use uncertain models based on the Bond Graph tool well adapted for the FC. Concretely, the parameters uncertainties are integrated in the model of evolution of the powers which is used for the detection of the beginning of the aging and the estimation of the degradation of the FC based on the causal and structural properties of the model. The generated model of degradation is used by an extended Kalman filter which allows the estimation of the state of health , the dynamics of the aging and the quantification of the uncertainty for any operating condition (of temperature, current and pressure). An Inverse First Order Reliability Method is then used for the prediction of the remaining useful life and the inherent uncertainty. The global method was validated on various sets of experimental data. Thanks to this set of tools, a control based on the inversion of an Energetic Macroscopic Representation (EMR) model with time varying parameters, robust to aging is developed based on the state of health estimation
Jouin, Marine. « Contribution au pronostic d'une pile à combustible de type PEMFC : approche par filtrage particulaire ». Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2027/document.
Texte intégralThe 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
Ginzarly, Riham. « Contribution à la modélisation et au pronostic des défaillances d'une machine synchrone à aimants permanents ». Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR038/document.
Texte intégralThe core of the work is to build an accurate model of the electrical machine where the prognostic technique is applied. In this thesis we started by a literature review on hybrid electric vehicles (HEV), the different types of electrical machine used in HEV’s and the different types of faults that may occur in those electrical machine. We also identify the useful monitoring parameters that are beneficial for those different types of faults. Then, a survey is presented where all the prognostic techniques that can be applied on this application are enumerated. The electromagnetic, thermal and vibration finite element model (FEM) of the permanent magnet machine is presented. The model is built at healthy operation and when a fault is integrated. The considered types of faults are:demagnetization, turn to turn short circuit and eccentricity. A confrontation between analytical and FEM (numerical method) for electromagnetic machine modeling is illustrated. Fault indicators where useful measured parameters forfault identification are recognized and useful features from the measured parameters are extracted; torque, temperature and vibration signal are elaborated for healthy and faulty states. The strategy of the adopted prognostic approach which is Hidden Markov Model (HMM) is explained. The technical aspect of the method is presented and the prognostic model is formulated. HMM is applied to detect and localize small scale fault small scale faults were where a systematic strategy is developed. The aging of the machine’s equipment,specially the sensitive ones that are the stator coil’s and the permanent magnet, is a very important matter for RUL calculation. An estimation strategy for RUL calculation is presented and discussed for those mentioned machine’s components. Closed loop configuration is very important; it is adopted by all available vehicle systems. Hence, the same previously mentioned steps are applied for a closed loop configuration too. A global model where the input of the machine’s FEM comes from the modeled inverter is built
Bolaers, Fabrice. « Contribution à l'étude et au développement d'un système intégré de suivi de l'endommagement de composants mécaniques sur machines tournantes : application à la détermination de la durée de vie résiduelle des paliers à roulements dans le cadre d'une maintenance conditionnelle par analyse vibratoire : Thèse pour le doctorat en sciences spécialité Génie mécanique ». Reims, 2002. http://www.theses.fr/2002REIMS024.
Texte intégralLechartier, Élodie. « Contribution au prognostic de pile à combustible PEMFC basé sur modèle semi-analytique ». Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2066/document.
Texte intégralThe current environmental concerns lead us to consider alternative solutions. The fuel cell can be one of them with numerous advantages, it presents however weaknesses, especially, its life duration which is too short. Face to this issue, we offer to apply the PHM to the PEMFC. For that, it is necessary to develop the prognostics for this application and the possibility of the on-line implementation on an industrial system. It was chosen to base the approach on a behavioral model in which the knowledge gaps are completed with the use of data. So, the approach proposed here, is hybrid. In this work, the behavioral model is studied on laps of time longer in order to finally introduce a prediction of a thousand of hours. Then, the online implementation on a real system is considered with a genericity and an applicability study. This work proposes a hybrid prognostics approach based on a behavioral model and study its implementation on an industrial system
Le, Thanh Trung. « Contribution to deterioration modeling and residual life estimation based on condition monitoring data ». Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT099/document.
Texte intégralPredictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models
Pasaguayo, Baez Liseth Victoria. « Degradation modeling and analysis for a microgripper for intracorporeal surgery ». Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD007.
Texte intégralThis research work deals with the degradation modeling and analysis for a microgripper for intracorporeal surgery. We first conducted a literature review to identify limitations for Prognostics and Health Management (PHM) implementation in medical microsystems. Secondly, a methodology based on risk management according to ISO 14971 for medical devices was developed to select the critical components of the microgripper. Thirdly, the data was collected on the microgripper system's kinematics, considering the angular position, velocity, acceleration, and jerk variables through a methodology that included data requirements, methods, and protocols. Once data were available, data analysis was performed, which allowed an understanding of the degradation behavior of the microgripper system, this understanding led to the identification of three distinct stages of degradation, which were categorized into three zones: safety, degradation, and critical. Moreover, it was identified the larger the closing range, the lower the number of cycles before failure occurs. Lastly, to predict the remaining useful life (RUL) of the microgripper system, a machine learning and deep learning approach was implemented. This approach consisted of fusing Gradient Boosting and Long short-term memory (LSTM) results to predict the RUL. The proposed approach performance was validated by the results of the RMSE, MAE, and R^2 metrics, as well as the online RUL implementation
Wang, Chu. « Deep learning-based prognostics for fuel cells under variable load operating conditions ». Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0530.
Texte intégralProton exchange membrane fuel cell (PEMFC) systems are suitable for various transportation applications thanks to their compact structure, high power density, low start/running temperature, and zero carbon emissions. High cost and lack of durability of PEMFC are still the core factors limiting their large-scale commercialization. In transportation applications, the deterioration of PEMFCs is aggravated by variable load conditions, resulting in a decrease in their Remaining Useful Life (RUL). Prognostics and health management (PHM) is an effective tool to forecast potential system risks, manage system control/maintenance schedules, improve system safety and reliability, extend system life, and reduce operation/maintenance costs. Prognostics is an important foundation and key support for PHM, and its core tasks include health indicator extraction, degradation trend prediction, and RUL estimation. The long-term degradation characteristics of PEMFC are concealed in variable load conditions, which increases the difficulty of health indicator extraction, reduces the accuracy of degradation prediction, and inhibits the reliability of life estimation. In view of this, the thesis work starts from modeling the degradation behavior of PEMFC under variable load conditions and carries out research work on health indicator extraction, short/long-term degradation trend prediction, RUL estimation and reliability evaluation
Robinson, Elinirina Iréna. « Filtering and uncertainty propagation methods for model-based prognosis ». Electronic Thesis or Diss., Paris, CNAM, 2018. http://www.theses.fr/2018CNAM1189.
Texte intégralIn this manuscript, contributions to the development of methods for on-line model-based prognosis are presented. Model-based prognosis aims at predicting the time before the monitored system reaches a failure state, using a physics-based model of the degradation. This time before failure is called the remaining useful life (RUL) of the system.Model-based prognosis is divided in two main steps: (i) current degradation state estimation and (ii) future degradation state prediction to predict the RUL. The first step, which consists in estimating the current degradation state using the measurements, is performed with filtering techniques. The second step is realized with uncertainty propagation methods. The main challenge in prognosis is to take the different uncertainty sources into account in order to obtain a measure of the RUL uncertainty. There are mainly model uncertainty, measurement uncertainty and future uncertainty (loading, operating conditions, etc.). Thus, probabilistic and set-membership methods for model-based prognosis are investigated in this thesis to tackle these uncertainties.The ability of an extended Kalman filter and a particle filter to perform RUL prognosis in presence of model and measurement uncertainty is first studied using a nonlinear fatigue crack growth model based on the Paris' law and synthetic data. Then, the particle filter combined to a detection algorithm (cumulative sum algorithm) is applied to a more realistic case study, which is fatigue crack growth prognosis in composite materials under variable amplitude loading. This time, model uncertainty, measurement uncertainty and future loading uncertainty are taken into account, and real data are used. Then, two set-membership model-based prognosis methods based on constraint satisfaction and unknown input interval observer for linear discete-time systems are presented. Finally, an extension of a reliability analysis method to model-based prognosis, namely the inverse first-order reliability method (Inverse FORM), is presented.In each case study, performance evaluation metrics (accuracy, precision and timeliness) are calculated in order to make a comparison between the proposed methods
Robinson, Elinirina Iréna. « Filtering and uncertainty propagation methods for model-based prognosis ». Thesis, Paris, CNAM, 2018. http://www.theses.fr/2018CNAM1189/document.
Texte intégralIn this manuscript, contributions to the development of methods for on-line model-based prognosis are presented. Model-based prognosis aims at predicting the time before the monitored system reaches a failure state, using a physics-based model of the degradation. This time before failure is called the remaining useful life (RUL) of the system.Model-based prognosis is divided in two main steps: (i) current degradation state estimation and (ii) future degradation state prediction to predict the RUL. The first step, which consists in estimating the current degradation state using the measurements, is performed with filtering techniques. The second step is realized with uncertainty propagation methods. The main challenge in prognosis is to take the different uncertainty sources into account in order to obtain a measure of the RUL uncertainty. There are mainly model uncertainty, measurement uncertainty and future uncertainty (loading, operating conditions, etc.). Thus, probabilistic and set-membership methods for model-based prognosis are investigated in this thesis to tackle these uncertainties.The ability of an extended Kalman filter and a particle filter to perform RUL prognosis in presence of model and measurement uncertainty is first studied using a nonlinear fatigue crack growth model based on the Paris' law and synthetic data. Then, the particle filter combined to a detection algorithm (cumulative sum algorithm) is applied to a more realistic case study, which is fatigue crack growth prognosis in composite materials under variable amplitude loading. This time, model uncertainty, measurement uncertainty and future loading uncertainty are taken into account, and real data are used. Then, two set-membership model-based prognosis methods based on constraint satisfaction and unknown input interval observer for linear discete-time systems are presented. Finally, an extension of a reliability analysis method to model-based prognosis, namely the inverse first-order reliability method (Inverse FORM), is presented.In each case study, performance evaluation metrics (accuracy, precision and timeliness) are calculated in order to make a comparison between the proposed methods
Silva, Sanchez Rosa Elvira. « Contribution au pronostic de durée de vie des systèmes pile à combustible de type PEMFC ». Thèse, 2016. http://depot-e.uqtr.ca/7860/1/031261937.pdf.
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