{"id":51931,"date":"2023-12-08T15:21:22","date_gmt":"2023-12-08T14:21:22","guid":{"rendered":"https:\/\/www.he-arc.ch\/?post_type=he-arc_project&p=51931"},"modified":"2023-12-08T15:21:24","modified_gmt":"2023-12-08T14:21:24","slug":"e-dap","status":"publish","type":"he-arc_project","link":"https:\/\/www.he-arc.ch\/en\/projets-recherche\/e-dap\/","title":{"rendered":"e-DAP Engineering Data Analytics Platform"},"content":{"rendered":"\n

Context and challenges<\/h2>\n\n\n\n

e-DAP (Engineering Data Analytics Platform) is a data archival and analysis tool that uses cut-edged digital techniques for data analysis and visualization combined with high-end mathematical and machine-learning models for predictive modelling. e-DAP can model the most important features for energy and industry markets and helps optimize processes and production.<\/p>\n\n\n\n

Objectives<\/h2>\n\n\n\n

Our objective is to develop an innovative and unique digital twin solution and infrastructure enabling a new digitization business centered on product licensing and product-enabled services. <\/p>\n\n\n\n

The proposed solution is innovative because it will combine physics modeling with real time\/condition parameters and data analytics to determine actual and probable health, control the production, and optimize the processes. <\/p>\n\n\n\n

Our solution will be deployed in the form of digital twins and production, tested and evaluated on different use cases, and including reuse of the models for further selected assets.<\/p>\n\n\n\n

Partners and funding<\/h2>\n\n\n\n

Afry GA Z\u00fcrich<\/a>
University of Applied Sciences of Northernwest Switzerland (FHNW)<\/a><\/p>\n\n\n\n

Funded by Innosuisse \u2013 Agence suisse pour l\u2019encouragement de l\u2019innovation<\/a><\/p>\n\n\n\n

Results<\/h2>\n\n\n\n

e-DAP is distributed as a Software as a Service (SaaS) application hosted on Microsoft Azure. It consists of software components developed by the e-DAP team as well as components provided by Azure. We manage each internal component in its own repository on Azure DevOps and the Azure components on the Azure portal. The software architecture is built according to the microservices paradigm. <\/p>\n\n\n\n

We adopt an agile methodology for software development using features from Azure DevOps such as the Boards for issue tracking and Pipelines for continuous integration and delivery (CI\/CD). e-DAP is evaluated and deployed for several use cases such as DSM Schweiz chemical processes; monitoring of the Gotthard Tunnel; Pulp Digester; Waste-Water Treatment Plant; Tall Oil; NASA Turbofan, Nuclear Power Plant of Leibstadt.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Project Manager<\/h2>\n\n\n\n
\n
\n
\n Dr. Hatem Ghorbel\n <\/div>\n
\n +41 32 930 22 06<\/a>\n <\/div>\n
\n Hatem.Ghorbel@he-arc.ch<\/a>\n <\/div>\n <\/div>\n<\/div>\n","protected":false},"author":6,"featured_media":51934,"template":"","he-arc_domain":[3],"he-arc_project_skill":[68,67],"he-arc_project_year":[42,43,44],"class_list":["post-51931","he-arc_project","type-he-arc_project","status-publish","has-post-thumbnail","hentry","he-arc_domain-ingenierie","he-arc_project_skill-analyse-de-donnees","he-arc_project_skill-technologies-dinteraction","he-arc_project_year-42","he-arc_project_year-43","he-arc_project_year-44"],"acf":[],"yoast_head":"\ne-DAP Engineering Data Analytics Platform - Haute-Ecole Arc<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.he-arc.ch\/en\/projets-recherche\/e-dap\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"e-DAP Engineering Data Analytics Platform - Haute-Ecole Arc\" \/>\n<meta property=\"og:description\" content=\"Context and challenges e-DAP (Engineering Data Analytics Platform) is a data archival and analysis tool that uses cut-edged digital techniques for data analysis and visualization combined with high-end mathematical and machine-learning models for predictive modelling. e-DAP can model the most important features for energy and industry markets and helps optimize processes and production. 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