Modèles stochastiques pour l'évaluation de politiques de maintenance sur des systèmes à dégradation graduelle

TROYES-SCD-UTT (103872102) / SudocSudocFranceF

Assessment of a maintenance model for a multi-deteriorating mode system

International audienceThis paper deals with maintenance policies for stochastically deteriorating systems which are subject to sudden changes in their degradation processes. The main aim is to assess the interest of using change mode monitoring information from a maintenance decision making point of view. Two condition-based maintenance policies are considered and compared, each of them adapted to a specific level of available information, with or without change mode monitoring.Numerical examples show that the time distribution of the change of deterioration rate and the difference between the two possible deterioration rates strongly influence the choice of the best decision rule structure

Assessment of a control limit maintenance decision rule for a multi deteriorating mode system

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Maintenance Conditionnelle en-ligne de systèmes à plusieurs modes de dégradation

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Maintenance policy on a finite time span for a gradually deteriorating system with imperfect improvements

International audienceThe study deals with a gradually deteriorating system such as a large structure. This system is studied over a finite time span where the finite horizon can be seen, for example, as an insurance deadline which requires a specific maintenance policy. Maintenance actions are assumed to be imperfect in this work. An improvement function is used to model the impact of the maintenance on the degradation level of the system. The improvement function is based in the virtual age model ARA1. A maintenance policy is then proposed in which maintenance actions are systematically performed at given maintenance dates, if the system has not already failed. It is assumed that in the event of a failure the system is not repaired. The system is then unavailable until the finite horizon. The proposed maintenance policy is assessed on the finite time span, and both maintenance dates and the number of maintenance actions are optimized

Assessment of an imperfect maintenance policy on a finite time span

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Imperfect condition-based maintenance on a finite time span for a gradually deteriorating system

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Modèle d'intégration de la maintenance conditionnelle et carte de contrôle multivariée : une analyse comparative univarié vs multivarié

International audienceSummary In order to reach the expected manufacturing process performance, common solutions are often implemented to solve problems that may affect the flow of production. It is noticed that the different services (quality, maintenance) act in an independent way to come up with disjoint solutions. With the digitalization of the industrial domain, information and data issued from the manufacturing process witnessed a growth in size and became more available, which encourages its exploitation under an integrated framework. The purpose of this article is to suggest an answer to this problem with a model based on multivariate control charts and not on the commonly used univariate control charts, interlinked with condition based maintenance. The profit per unit of time is considered an indicator of performance.Afin d'atteindre les performances attendues d'un système industriel, des solutions sont souvent implémentées pour remédier aux problèmes qui peuvent nuire au bon fonctionnement du processus de production. Ceci étant, Il est fréquent que les différents services (qualité, maintenance…) agissent indépendamment les uns des autres. Pourtant, avec la digitalisation, la quantité d'information récoltée au niveau du système industriel est devenue de plus en plus importante ce qui encourage son exploitation en adoptant une approche intégrée. L'objet de cet article est de proposer une réponse à cette problématique en proposant un modèle qui s'appuie sur l'utilisation des cartes de contrôle multivariées au lieu des cartes univariées. Le gain par unité de temps sera considéré comme un indicateur de performance

Quality of complex system reliability prevision assessment

L’objectif de cet article est de proposer une méthodologie de construction d’un modèle de prévision de la qualité des systèmes complexes tout au long de leur cycle de vie. Cette méthodologie est basée sur la définition de la qualité des systèmes complexes à travers différents facteurs en tenant compte des spécificités des entreprises. Le modèle obtenu avec cette méthodologie aide les ingénieurs qualité et le chef de projet à avoir une vue objective de la qualité d’un système au cours de son développement et sa future qualité en service. Cette approche est illustrée par son application à la prévision de la fiabilité au sein de l’entreprise MBDA, entreprise du secteur aéronautique et défense.In this paper, we propose a methodology to define a model to predict complex system quality all along its lifecycle. This methodology is based on a definition of complex system’s quality through factors and allows taking into account the specificities of companies. The model obtained with this methodology helps quality practitioners and project manager to have an objective view of complex system quality during development and to predict this quality in use. This approach is illustrated through its application to design a model to assess the quality of complex systems’ reliability prevision during development, for MBDA, an aeronautic and defense group

Ensemble Learning for Multi-Label Classification with Unbalanced Classes: A Case Study of a Curing Oven in Glass Wool Production

The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry. The massive acquisition of data, in an industrial context, brings with it a number of challenges to guarantee its quality and reliability, and to ensure that the results of data analysis and modelling are accurate, reliable, and reflect the real phenomena being studied. Common problems encountered with real industrial databases are missing data, outliers, anomalies, unbalanced classes, and non-exhaustive historical data. Unlike papers present in the literature that respond to those problems in a dissociated way, the work performed in this article aims to address all these problems at once. A comprehensive framework for data flow encompassing data acquisition, preprocessing, and machine class classification is proposed. The challenges of missing data, outliers, and anomalies are addressed with critical and novel class outliers distinguished. The study also tackles unbalanced class classification and evaluates the impact of missing data on classification accuracy. Several machine learning models for the operating state classification are implemented. The study also compares the performance of the proposed framework with two existing methods: the Histogram Gradient Boosting Classifier and the Extreme Gradient Boosting classifier. It is shown that using “hard voting” ensemble learning methods to combine several classifiers makes the final classifier more robust to missing data. An application is carried out on data from a real industrial dataset. This research contributes to narrowing the theory–practice gap in leveraging IIoT technologies, offering practical insights into data analytics implementation in real industrial scenarios