465 research outputs found

    Problem solving methods as Lessons Learned System instrumentation into a PLM tool

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    Among the continuous improvement tools of the performance in enterprise, the experience feedback represents undoubtedly an effective lever of progress by offering important prospects for a progression in almost all the industrial sectors. However, several reserves to its use slow down the diffusion of its employment. We are interested in the installation of experience feedback system in a partner enterprise. In this paper, we propose an instrumentation of a Lessons Learned System (LLS) by problem solving methods (PSM) and its integration with a product lifecycle management (PLM). These proposals support an improvement of LLS performance and a facility of his application

    Characterisation of collaborative decision making processes

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    This paper deals with the collaborative decision making induced or facilitated by Information and Communication Technologies (ICTs) and their impact on decisional systems. After presenting the problematic, we analyse the collaborative decision making and define the concepts related to the conditions and forms of collaborative work. Then, we explain the mechanisms of collaborative decision making with the specifications and general conditions of collaboration using the modelling formalism of the GRAI method. Each specification associated to the reorganisation of the decisional system caused by the collaboration is set to the notion of decision-making centre. Finally, we apply this approach to the e-maintenance field, strongly penetrated by the ICTs, where collaborations are usual. We show that the identified specifications allow improving the definition and the management of collaboration in e-maintenance

    Data validation: a case study for a feed-drive monitoring

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    The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of requests on control, reliability, availability of machine-tools and on work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring, 
 The improvement of monitoring of the main machine-tools devices, the feed-drives and the spindles that drive the cutting process, can be realised by estimating their fault sensitive physical parameters from their continuous-time model. We have chosen to use a continuous-time ARX model. We particularly focus on slow time varying phenomena. This estimation should run while there is no machining process to avoid false detection of faults on the machine due to the cutting process. High speed motions, that occur at least for each tool exchange, are exploited. Some functional constraints require the use of an off-line estimation method, we have chosen an ordinary least squares method. Estimating the physical parameters is insufficient to obtain an efficient monitoring. A measurement analysis and validation are necessary as the validation of the estimated physical parameters. An approach of the measurement and physical parameter estimation validation for a NC machine-tool feed-drive is proposed

    MaĂźtrise des risques dans le processus de rĂ©ponse Ă  appel d’offres

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    Un appel d'offres (AO) permet Ă  un client d’émettre une demande de travaux ou de services envers des prestataires potentiels et de faire ensuite, par analyse des rĂ©ponses reçues, le choix de celui qui sera retenu. Du point de vue du soumissionnaire, il existe plusieurs risques au moment de rĂ©pondre car il doit Ă©laborer une rĂ©ponse sur un dĂ©veloppement futur. De nature diffĂ©rente, ces risques peuvent ĂȘtre regroupĂ©s en catĂ©gories. Nous proposons une typologie des risques sur laquelle nous nous appuyons afin d'assister le prestataire lors du processus de rĂ©ponse Ă  appel d’offre (PRAO) via une mĂ©thodologie d'aide Ă  la dĂ©cision fondĂ©e sur l’expĂ©rience acquise dans le dĂ©roulement des projets passĂ©s pour dĂ©tecter, rendre compte et minimiser les risques du PRAO en cours

    Integration of experience feedback into the product lifecycle: an approach to best respond to the bidding process

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    Bidding process allows a client to choose a bidder to realize an embodiment of work, supply or service. From the bidder point of view, there are several obvious risks when responding because he bets on a future development that hasn’t been yet realized. We propose to assist the bidder with decision support tools based on past experiences to detect, report and minimize these potential risks. In this paper, we present the definition of a conceptual architecture to integrate experience feedback into the product lifecycle taking into account all stages of product lifecycle to best respond new bidding processes

    Analyse du cycle de vie du produit par retour d'expérience: proposition d'un outil d'assistance au processus de réponse à appel d'offres

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    Ce travail a pour objectif d’établir les principes d’un outil d’aide Ă  la dĂ©cision pour l’instrumentation du processus de rĂ©ponse aux appels d’offre (PRAO) permettant au maĂźtre d’Ɠuvre de conduire efficacement ce processus en minimisant les risques encourus. Le but est de dĂ©finir un outil interactif utilisant l’expĂ©rience acquise dans le dĂ©roulement des projets passĂ©s pour dĂ©tecter, rendre compte et minimiser les risques du processus en cours. Pour cela, nous dĂ©finissons le PRAO et explicitons les diffĂ©rents risques susceptibles d’affecter sa rĂ©alisation, puis nous proposons une architecture intĂ©grant ce processus et le retour d’expĂ©rience (REX). Enfin, nous dĂ©finissons une instrumentation de cette mĂ©thodologie Ă  partir d’un outil informatique, nommĂ© BP_IAT (Bid Process Interactive Analysis Tool), permettant de prendre en compte les expĂ©riences passĂ©es pour rĂ©pondre Ă  un nouvel appel d’offre en minimisant les risques potentiels lors du choix d’un concept de la solution en cours de dĂ©veloppement

    Analyse des systÚmes - Sûreté de fonctionnement

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    La complexitĂ© croissante des organisations et systĂšmes industriels et la recherche rĂ©currente d’une meilleure compĂ©titivitĂ© forcent les entreprises et gestionnaires d’équipements Ă  une Ă©valuation systĂ©matique et quasi continue des performances. La performance est multidimensionnelle. DĂ©clinĂ©e suivant des attributs de coĂ»t, qualitĂ©, dĂ©lai,
, des critĂšres de productivitĂ©, flexibilitĂ©, robustesse,
, des aspects environnementaux, sociaux, sociĂ©taux,
, elle doit ĂȘtre Ă©valuĂ©e sur l'ensemble du cycle de vie du systĂšme ou des produits rĂ©alisĂ©s. Cette diversitĂ©, motivĂ©e par une logique socio-Ă©conomique de dĂ©veloppement durable, gĂ©nĂšre un besoin fort en mĂ©thodologies, techniques et outils pour aider aux choix des dĂ©cideurs dans les phases de conception, de dĂ©veloppement ou d’exploitation des produits et systĂšmes. Nombreuses sont les rĂ©ponses ; nombreux aussi sont les ouvrages et articles spĂ©cialisĂ©s qui exposent celles-ci, depuis un Ă©tat dĂ©taillĂ© de toutes les formes d’aide jusqu’à la prĂ©sentation prĂ©cise d’outil ou de technique particuliĂšre. L’objectif de l'article est de fournir une approche efficace d’analyse d’un systĂšme afin d'estimer et d'Ă©valuer la performance de celui-ci. Les Ă©lĂ©ments mĂ©thodologiques qui garantissent une analyse rationnelle du systĂšme et de ses performances seront mis en exergue, focalisant sur les aspects sĂ»retĂ© de fonctionnement considĂ©rĂ©s dĂšs les Ă©tapes de conception ; la recherche de performance est, en effet, corrĂ©lĂ©e au souci constant d‘amĂ©lioration de la disponibilitĂ© opĂ©rationnelle du systĂšme et d’optimisation de son coĂ»t global de possession

    Modeling dynamic reliability using dynamic Bayesian networks

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    This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this is the dynamic reliability. We consider discrete time case, that is the state of the system as well as the state of process variables are observed or measured at discrete time instants. A mathematical tool that shows interesting properties for modeling and analyzing this problem is the so called Dynamic Bayesian Networks (DBN) that permit graphical representation of stochastic processes. Furthermore their learning and inference capabilities can be exploited to take into account experimental data or expert’s knowledge. We will show that a complex interaction between system and process on one hand and between system, process and exogenous perturbation on the other hand can simply be represented graphically by a dynamic Bayesian network. With their extended tool, known as influence diagrams (ID) that integrate actions or decisions possibilities, one can analyze and optimize a maintenance policy and/or make reactive decision during an accident by simulating different scenarios of its evolution for instance

    Graph-based reasoning in collaborative knowledge management for industrial maintenance

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    Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach is applied to a case study focusing on the maintenance management of a rotary machinery system

    Proposition d'amélioration d'un systÚme de retour d'expérience

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    L’objet de cette communication est de prĂ©senter des travaux portant sur le dĂ©ploiement d’un systĂšme de retour d’expĂ©rience dans un progiciel PLM (Product Lifecycle Management). Ces travaux sont rĂ©alisĂ©s en partenariat avec la sociĂ©tĂ© Saft Bordeaux, spĂ©cialisĂ©e dans la conception et la fabrication de systĂšmes de batteries complexes. Nous commençons par dĂ©finir la notion de systĂšme de retour d’expĂ©rience avec ses trois phases clefs (capitalisation, traitement et exploitation) qui le composent. Puis, Ă  l’aide d’un audit rĂ©alisĂ© auprĂšs d’une trentaine d’acteurs impliquĂ©s dans le dĂ©veloppement des produits, nous analysons les pratiques et outils actuellement employĂ©s Ă  la Saft. De cette analyse, nous identifions les freins et les attentes des acteurs pour pouvoir rĂ©aliser un retour d’expĂ©rience efficient. Enfin, face Ă  ces rĂ©sultats, nous prĂ©sentons les principes de la solution mise en oeuvre et les intĂ©rĂȘts d’avoir couplĂ© un systĂšme REx (Retour d'ExpĂ©rience) Ă  un PLM. Nous concluons en prĂ©sentant les perspectives importantes qu’offre un tel travail
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