Traditional maintenance approaches are under-optimized. 20% savings on total maintenance costs and improvements of Overall Equipment Effectiveness (OEE) are achievable. Most of the predictive maintenance solutions only offer analytics capabilities. Operators have to assert themselves the maintenance action to implement and to manually balance between multiple variables to decide on the optimal implementation time. The UPTIME solution is built upon the predictive maintenance and four integrated technological pillars, i.e. Industrial Internet, IoT, Big Data and Proactive Computing. The collect and aggregation of data is supported by the use of technological standards as the semantic web standards. UPTIME is a fully adaptable, open and modular end-to-end predictive maintenance solution for industry, from sensor data collection to optimal planning. Through advanced prognostic algorithms and on-line failure mode analysis, it predicts upcoming failures. Then, decision algorithms recommend the best action to optimize maintenance and to improve OEE. UPTIME can be implemented in any industry regardless of its processes, products and physical models. UPTIME is being implemented for 3 first industrial use cases: Mobile assets for aeronautics, white goods production system and cold rolling mill lines.
The presentation will include a show case to highlight the features and benefits of UPTIME: Health assessment through measured data will be simulated according various scenarios, the data will be computed by UPTIME, which will propose maintenance possible actions ordered according different criteria.
Yves Keraron, Founder and CEO of ISADEUS (Innovation Standards And Digital Engineering Using Semantics), has continuously worked for innovation, first in nuclear engineering, then in information systems for technical data and documentation to support the activities of the lifecycle of complex products or production systems. ISADEUS has been recently involved in European H2020 projects for the Factory of the Future, using new technological standards, especially web standards for industry : FALCON and UPTIME. He is an expert in the National Commission IDMI (Ingénierie des Données et de Modèles pour l’Industrie) of AFNOR. He is active in the Industrial Internet through the use of advanced web standards to satisfy the needs of industry to manage the growing volume of data. He is graduated of Ecole Centrale des Arts et Manufactures (Ecole Centrale Paris) and completed a PhD on the impact of digital technologies on the relationships between Technical, information and human systems Twitter : @YvesKeraron
Karl Hribernik studied Computer Science at the University of Bremen. He is manager of the department Intelligent ICT for Co-operative Production at BIBA. His research focus is on the semantic interoperability of heterogeneous and dynamic data sources in closed-loop and item-level Product Lifecycle Management. He is currently the coordinator of the H2020 Factories of the Future project UPTIME – Unified Predicitive Maintenance System.
Ntalaperas Dimitris was born in Athens, Greece in 1980. He received his BSc and MSc from the Computer Engineering and Informatics Department of the Polytechnic School of the University of Patras. His Master Thesis was in simulation of quantum computers implemented in doped semiconducting materials. He conducted research and worked in the areas quantum computing, simulation of physical system, boundary element method, big data, text processing, data anonymization and semantic web. He held a number of positions as a researcher or software engineer in various institutions and companies (RACTI 2005-2008, BemSands 2008-2010, Technological Educational Institute of Patras 2009-2010, University of Patras 2008-2010, Ubitech 2012-). He participated in a number of EC and National co-funded projects in the areas of e-Health (Linked2Safety, DISYS, CAREPATHS, GRANATUM) being involved mainly in the tasks of Data Anonymization, Named Entity Recognition and Rule based Decision Support Systems. In the context of Marie Curie Actions, he also participated in the SAGE-CARE Project during which he seconded Hochschule Darmstadt and Università degli Studi di Napoli Federico II and was trained in the areas of Semantic Web and High Performance Computing.