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(RSM) Rail Digital Twins For Rolling Stock Maintenance Analytics: An Industrial AI approach
Dr. Diego Galar , Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering, LTU, Luleå University

• Information extraction to assess the overall condition of the whole system. Integrating asset information during the entire lifecycle. Gaining accurate health assessment of the whole system.
• Augmenting datasets before training data-driven algorithms. For this purpose Data covering a wider range of scenarios can be obtained by synthetic data generated by physics-based models. These models need to be realistic and provide meaningful and comparable information about the behaviour of the system under observation.
• Learn how industrial AI can help the use/owner/maintainer/designer to perform a virtual commissioning of the asset where it is digitized and virtualized and produce a digital twin containing both data driven and physical information.

Sep 23, 2020 11:00 AM in London

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Speakers

Dr. Diego Galar
Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering @Luleå University of Technology, Sweden
Dr. Diego Galar is Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova. He is also principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport.