Literatura académica sobre el tema "Prognostic de défaillance"
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Artículos de revistas sobre el tema "Prognostic de défaillance"
Adegboro, B., T. O. Musa-Booth, I. N. Mba, R. R. Ibrahim, N. Medugu, S. A. Abayomi y M. Babazhitsu. "A systematic review of clinical characteristics, co-morbidities and outcomes of COVID-19 in children and adolescents". African Journal of Clinical and Experimental Microbiology 23, n.º 4 (23 de octubre de 2022): 335–44. http://dx.doi.org/10.4314/ajcem.v23i4.2.
Texto completoGOURIVEAU, Rafael, Kamal MEDJAHER, Emmanuel RAMASSO y Noureddine ZERHOUNI. "PHM – Prognostics and health management - De la surveillance au pronostic de défaillances de systèmes complexes". Maintenance, abril de 2013. http://dx.doi.org/10.51257/a-v1-mt9570.
Texto completoTesis sobre el tema "Prognostic de défaillance"
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.
Texto completoState-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
Nguyen, Thi Bich Lien. "Approche statistique pour le pronostic de défaillance : application à l'industrie du semi-conducteur". Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4310/document.
Texto completoThis thesis develops a fault prognosis approach for Discrete Manufacturing Processes. A method of raw health index extraction from a data tensor, called Significant Points was developped and validated on an illustrative example. The generated index is later processed by a new method, called Percentile Method, which allows to generate the monotonic profiles from the raw health index. These profiles are then modelled by a Gamma process, and the aggregate probability density function introduced in this work allowed to estimate the Remaining Useful Life (RUL) in a confidence interval that ensures a safety margin for industrial users. The proposed method is applied successfully on the experimental data of industrial production machines
Aissaoui, Nadia. "Analyse du rôle des fonctions ventriculaires longitudinales dans les défaillances cardio-circulatoires graves". Thesis, Paris Est, 2013. http://www.theses.fr/2013PEST0100.
Texto completoDespite advances in management and therapies, cardiogenic shock remains a clinical challenge with high mortality rates.The analysis of left and right ventricular functions and filling pressures are important in this context because they had diagnostic and prognostic consequences with impact in therapeutic decisions. Nevertheless, the assessment of myocardial function remains difficult for physiopathologic and technical reasons. The parameters of longitudinal ventricular function (LgVF) could have an interest in this context because they permit a direct assessment of a major component of ventricular mechanics whereas ejection fraction remains a global evaluation. These indices were assessed in chronic and stable heart failure patients and were found to have prognostic and diagnostic interests. Though, they were not evaluated in the context of acute and severe cardio-circulatory failures
Martin, Florent. "Pronostic de défaillances de pompes à vide - Exploitation automatique de règles extraites par fouille de données". Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENA011.
Texto completoThis thesis presents a symbolic rule-based method that addresses system prognosis. It also details a successful application to complex vacuum pumping systems. More precisely, using historical vibratory data, we first model the behavior of the pumps by extracting a given type of episode rules, namely the First Local Maximum episode rules (FLM-rules). The algorithm that extracts FLM-rules also determines automatically their respective optimal temporal window, i.e. the temporal window in which the probability of observing the premiss and the conclusion of a rule is maximum. A subset of the extracted FLM-rules is then selected in order to further predict pumping system failures in a vibratory data stream context. Our contribution consists in selecting the most reliable FLM-rules, continuously matching them in a data stream of vibratory data and building a forecast time interval using the optimal temporal windows of the FLM-rules that have been matched
Kanazy, Redouane. "Pronostic des événements de défaillance basé sur les réseaux de Petri Temporels labellisés". Thesis, Lyon, 2020. http://theses.insa-lyon.fr/publication/2020LYSEI132/these.pdf.
Texto completoThe deployment of decision-support tools increases agility efficiency while reducing the cost of maintaining proper operation. Accidental or intentional shutdowns have disastrous and costly consequences. The scientific community of discrete event systems (DES), has been interested in the cause-effect relationships between certain nominal and dysfunctional states, to propose solutions responding to this problem. Our work is part of the framework of a steering of a system subject to failure events (FE). We have developed a model-based prognostic approach, which predicts the earliest date of occurrence of an FE, allowing the operator to plan repair interventions on components before altering the proper functioning. We modeled the system using labeled temporal Petri nets (RdPTL), since the analysis of its behavioral model is based on the sequentiality and date of occurrence of events. We have represented these dynamics through modeling in a context of mode analysis, limited to 3 modes of operation (nominal, degraded and critical). From its accessibility graph, we have built a prognosticator, which allows us to identify all the sequences of events ending with an FE. We used the notion of parameterization of the system states i.e. the introduction of a clock and a system of clock inequation (SE) for each state of the system. The states obtained from the discretization of time are then grouped in a single state and the SE will determine the values of the clocks. The prognosis cannot always be guaranteed. We then established the prognosticability property, to distinguish the sequences that are prognosticable from those that are not. To validate our approach, we chose a battery cell as a benchmark and the INA tool to generate the accessibility graph
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.
Texto completoThe 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
Mosallam, Ahmed. "Remaining useful life estimation of critical components based on Bayesian Approaches". Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2069/document.
Texto completoConstructing prognostics models rely upon understanding the degradation process of the monitoredcritical components to correctly estimate the remaining useful life (RUL). Traditionally, a degradationprocess is represented in the form of physical or experts models. Such models require extensiveexperimentation and verification that are not always feasible in practice. Another approach that buildsup knowledge about the system degradation over time from component sensor data is known as datadriven. Data driven models require that sufficient historical data have been collected.In this work, a two phases data driven method for RUL prediction is presented. In the offline phase, theproposed method builds on finding variables that contain information about the degradation behaviorusing unsupervised variable selection method. Different health indicators (HI) are constructed fromthe selected variables, which represent the degradation as a function of time, and saved in the offlinedatabase as reference models. In the online phase, the method estimates the degradation state usingdiscrete Bayesian filter. The method finally finds the most similar offline health indicator, to the onlineone, using k-nearest neighbors (k-NN) classifier and Gaussian process regression (GPR) to use it asa RUL estimator. The method is verified using PRONOSTIA bearing as well as battery and turbofanengine degradation data acquired from NASA data repository. The results show the effectiveness ofthe method in predicting the RUL
Deng, Yingjun. "Degradation modeling based on a time-dependent Ornstein-Uhlenbeck process and prognosis of system failures". Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0004/document.
Texto completoThis thesis is dedicated to describe, predict and prevent system failures. It consists of four issues: i) stochastic degradation modeling, ii) prognosis of system failures, iii) failure level estimation and iv) maintenance optimization. The time-dependent Ornstein-Uhlenbeck (OU) process is introduced for degradation modeling. The time-dependent OU process is interesting from its statistical properties on controllable mean, variance and correlation. Based on such a process, the first passage time is considered as the system failure time to a pre-set failure level. Different methods are then proposed for the prognosis of system failures, which can be classified into three categories: analytical approximations, numerical algorithms and Monte-Carlo simulation methods. Moreover, the failure level is estimated from the lifetime distribution by solving inverse first passage problems. This is to make up the potential gap between failure and degradation records to reinforce the prognosis process via first passage problems. From the prognosis of system failures, the maintenance optimization for a continuously monitored system is performed. By introducing first passage problems, the arrangement of preventive maintenance is simplified. The maintenance decision rule is based on a virtual failure level, which is solution of an optimization problem for proposed objective functions
Gay, Antonin. "Pronostic de défaillance basé sur les données pour la prise de décision en maintenance : Exploitation du principe d'augmentation de données avec intégration de connaissances à priori pour faire face aux problématiques du small data set". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0059.
Texto completoThis CIFRE PhD is a joint project between ArcelorMittal and the CRAN laboratory, with theaim to optimize industrial maintenance decision-making through the exploitation of the available sources of information, i.e. industrial data and knowledge, under the industrial constraints presented by the steel-making context. Current maintenance strategy on steel lines is based on regular preventive maintenance. Evolution of preventive maintenance towards a dynamic strategy is done through predictive maintenance. Predictive maintenance has been formalized within the Prognostics and Health Management (PHM) paradigm as a seven steps process. Among these PHM steps, this PhD's work focuses on decision-making and prognostics. The Industry 4.0 context put emphasis on data-driven approaches, which require large amount of data that industrial systems cannot ystematically supply. The first contribution of the PhD consists in proposing an equation to link prognostics performances to the number of available training samples. This contribution allows to predict prognostics performances that could be obtained with additional data when dealing with small datasets. The second contribution of the PhD focuses on evaluating and analyzing the performance of data augmentation when applied to rognostics on small datasets. Data augmentation leads to an improvement of prognostics performance up to 10%. The third contribution of the PhD consists in the integration of expert knowledge into data augmentation. Statistical knowledge integration proved efficient to avoid performance degradation caused by data augmentation under some unfavorable conditions. Finally, the fourth contribution consists in the integration of prognostics in maintenance decision-making cost modeling and the evaluation of prognostics impact on maintenance decision cost. It demonstrates that (i) the implementation of predictive maintenance reduces maintenance cost up to 18-20% and ii) the 10% prognostics improvement can reduce maintenance cost by an additional 1%
Robinson, Elinirina Iréna. "Filtering and uncertainty propagation methods for model-based prognosis". Electronic Thesis or Diss., Paris, CNAM, 2018. http://www.theses.fr/2018CNAM1189.
Texto completoIn 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
Libros sobre el tema "Prognostic de défaillance"
Analysis of Failure and Survival Data. Chapman & Hall/CRC, 2002.
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