{"id":13317,"date":"2022-02-25T17:23:39","date_gmt":"2022-02-25T16:23:39","guid":{"rendered":"https:\/\/fondation-grenoble-inp.fr\/?post_type=projet&#038;p=13317"},"modified":"2022-07-20T15:06:58","modified_gmt":"2022-07-20T13:06:58","slug":"datamining-et-machine-learning-appliques-a-la-conception-des-machines-hydrauliques","status":"publish","type":"projet","link":"https:\/\/fondation-grenoble-inp.fr\/en\/projet\/datamining-et-machine-learning-appliques-a-la-conception-des-machines-hydrauliques\/","title":{"rendered":"Datamining and Machine Learning applied to the design of hydraulic machines"},"content":{"rendered":"<p><strong>Axe de recherche&nbsp;: <\/strong>Contr\u00f4le-commande, monitoring, mesure et capteurs<\/p>\n\n\n\n<p><strong>Porteurs du projet\u00a0<\/strong>: <a href=\"https:\/\/www.linkedin.com\/in\/yann-laurant-phd-67417577\/\">Yann Laurant<\/a>, General Electric Renewable Energy &amp; <a href=\"https:\/\/www.linkedin.com\/in\/prdgeorges\/\" target=\"_blank\" rel=\"noreferrer noopener\">Didier Georges<\/a>, Laboratoire du GIPSA-lab<\/p>\n\n\n\n<p><strong>Organismes associ\u00e9s au projet<\/strong> : <a href=\"http:\/\/www.gipsa-lab.fr\" target=\"_blank\" rel=\"noreferrer noopener\">GIPSA-lab<\/a> &amp; <a href=\"https:\/\/www.ge.com\/fr\/\" target=\"_blank\" rel=\"noreferrer noopener\">General Electric Renewable Energy<\/a><\/p>\n\n\n\n<p><strong>Le projet<\/strong> :<\/p>\n\n\n\n<p>Lors de la conception d\u2019une nouvelle machine hydraulique, une des \u00e9tapes les plus importantes et plus couteuses consiste \u00e0 construire un mod\u00e8le r\u00e9duit sur lequel des essais exp\u00e9rimentaux peuvent \u00eatre r\u00e9alis\u00e9s. Pendant ces essais, divers capteurs distribu\u00e9s dans la turbine mesurent ses caract\u00e9ristiques de fonctionnement (e.g., charge, d\u00e9bit, vitesse de rotation, rendement, etc.) et, \u00e9tant donn\u00e9 que la g\u00e9om\u00e9trie de la machine est elle aussi bien connue, cela signifie qu\u2019une quantit\u00e9 significative de donn\u00e9es (physiques et g\u00e9om\u00e9triques) est disponible \u00e0 la fin de chaque essai. L\u2019exploitation de ces donn\u00e9es, notamment par des techniques de <em>datamining<\/em> and <em>machine learning<\/em>, peut donc servir \u00e0 identifier des relations et construire des mod\u00e8les de pr\u00e9diction des caract\u00e9ristiques de fonctionnement d\u2019une turbine sans avoir plus besoin \u00e0 des essais physiques, en r\u00e9duisant ainsi le co\u00fbt et le temps n\u00e9cessaire \u00e0 la conception d\u2019une nouvelle machine.\u00a0<\/p>\n\n\n\n<p><strong>Objectifs du projet<\/strong> :<\/p>\n\n\n\n<p>Les trois objectifs principaux de ce projet de recherche sont\u202f:&nbsp;<\/p>\n\n\n\n<ul><li>Analyser les donn\u00e9es exp\u00e9rimentales de dizaines de turbines hydrauliques \u00e9tudi\u00e9es exp\u00e9rimentalement par General Electric pour ensuite \u00e9laborer une base de donn\u00e9es adapt\u00e9e \u00e0 l\u2019exploitation par des techniques de <em>datamining<\/em>\u202f;\u00a0<\/li><li>Cr\u00e9er des mod\u00e8les du type <em>surrogate<\/em> \u00e0 partir de cette base de donn\u00e9es en utilisant plusieurs techniques d\u2019apprentissage supervis\u00e9es<em> <\/em>(e.g., AdaBoost, RBF, r\u00e9seaux de neurones artificiels et \u00e0 convolution) ainsi que des m\u00e9thodes d\u2019optimisation de leurs <em>hyperparam\u00e8tres<\/em> ;\u00a0<\/li><li>Evaluer la fiabilit\u00e9 de la pr\u00e9diction de chaque mod\u00e8le <em>surrogate <\/em>et d\u00e9terminer ceux qui sont appropri\u00e9s \u00e0 la conception des machines hydrauliques plus performantes.\u00a0<\/li><\/ul>\n\n\n\n<p><strong>R\u00e9sultat attendu<\/strong> :<\/p>\n\n\n\n<p>L\u2019objectif final du projet sera de d\u00e9velopper des nouveaux outils bas\u00e9s notamment sur des mod\u00e8les de pr\u00e9diction con\u00e7ues \u00e0 partir de l\u2019exploitation des bases de donn\u00e9es exp\u00e9rimentales existantes par des techniques de <em>machine learning<\/em>. Ces outils pourront, ensuite, remplacer les essais exp\u00e9rimentaux et\/ou simulations num\u00e9riques, en r\u00e9duisant ainsi le co\u00fbt associ\u00e9 \u00e0 la conception des nouvelles machines hydrauliques plus performantes.\u00a0<\/p>\n\n\n\n<p><strong>Planning<\/strong> :<\/p>\n\n\n\n<p>Octobre 2021 &#8211; D\u00e9cembre 2022<\/p>","protected":false},"excerpt":{"rendered":"<p>Axe de recherche&nbsp;: Contr\u00f4le-commande, monitoring, mesure et capteurs Porteurs du projet\u00a0: Yann Laurant, General Electric Renewable Energy &amp; Didier Georges, Laboratoire du GIPSA-lab Organismes associ\u00e9s au projet : GIPSA-lab &amp; General Electric Renewable Energy Le projet : Lors de la conception d\u2019une nouvelle machine hydraulique, une des \u00e9tapes les plus importantes et plus couteuses consiste [&hellip;]<\/p>","protected":false},"featured_media":14137,"template":"","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Datamining et Machine Learning appliqu\u00e9s \u00e0 la conception des machines hydrauliques - 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