465 research outputs found
Problem solving methods as Lessons Learned System instrumentation into a PLM tool
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
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
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 dâoffres
Un appel d'offres (AO) permet Ă un client dâĂ©mettre 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 dâoffre (PRAO) via une mĂ©thodologie d'aide Ă la dĂ©cision fondĂ©e sur lâexpĂ©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
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 hasnât 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
Ce travail a pour objectif dâĂ©tablir les principes dâun outil dâaide Ă la dĂ©cision pour lâinstrumentation du processus de rĂ©ponse aux appels dâoffre (PRAO) permettant au maĂźtre dâĆuvre de conduire efficacement ce processus en minimisant les risques encourus. Le but est de dĂ©finir un outil interactif utilisant lâexpĂ©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 dâaffecter sa rĂ©alisation, puis nous proposons une architecture intĂ©grant ce processus et le retour dâexpĂ©rience (REX). Enfin, nous dĂ©finissons une instrumentation de cette mĂ©thodologie Ă partir dâun 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 dâoffre en minimisant les risques potentiels lors du choix dâun concept de la solution en cours de dĂ©veloppement
Analyse des systÚmes - Sûreté de fonctionnement
La complexitĂ© croissante des organisations et systĂšmes industriels et la recherche rĂ©currente dâune meilleure compĂ©titivitĂ© forcent les entreprises et gestionnaires dâĂ©quipements 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,âŠ, des critĂšres de productivitĂ©, flexibilitĂ©, robustesse,âŠ, des aspects environnementaux, sociaux, sociĂ©taux,âŠ, 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 dâexploitation 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 dâaide jusquâĂ la prĂ©sentation prĂ©cise dâoutil ou de technique particuliĂšre.
Lâobjectif de l'article est de fournir une approche efficace dâanalyse dâun 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 dâamĂ©lioration de la disponibilitĂ© opĂ©rationnelle du systĂšme et dâoptimisation de son coĂ»t global de possession
Modeling dynamic reliability using dynamic Bayesian networks
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 expertâs 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
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
Lâobjet de cette communication est de prĂ©senter des travaux portant sur le dĂ©ploiement dâun systĂšme de retour dâexpĂ©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 dâexpĂ©rience avec ses trois phases clefs (capitalisation, traitement et exploitation) qui le composent. Puis, Ă lâaide dâun audit rĂ©alisĂ© auprĂšs dâune trentaine dâacteurs 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 dâexpĂ©rience efficient.
Enfin, face Ă ces rĂ©sultats, nous prĂ©sentons les principes de la solution mise en oeuvre et les intĂ©rĂȘts dâavoir couplĂ© un systĂšme REx (Retour d'ExpĂ©rience) Ă un PLM. Nous concluons en prĂ©sentant les perspectives importantes quâoffre un tel travail
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