Universiteit Twente, Faculty of Engineering Technology (ET), Applied Mechanics & Data Analysis (AMDA)
Universiteit Twente, Faculty of Engineering Technology (ET), Applied Mechanics & Data Analysis (AMDA)
2 Projects, page 1 of 1
assignment_turned_in Project2022 - 9999Partners:Technische Universiteit Delft, Faculteit Technische Natuurwetenschappen, Chemical Engineering, Technische Universiteit Delft, Faculteit Luchtvaart- en Ruimtevaarttechniek, Universiteit Twente, Faculty of Engineering Technology (ET), Universiteit Twente, Technische Universiteit Eindhoven - Eindhoven University of Technology +12 partnersTechnische Universiteit Delft, Faculteit Technische Natuurwetenschappen, Chemical Engineering,Technische Universiteit Delft, Faculteit Luchtvaart- en Ruimtevaarttechniek,Universiteit Twente, Faculty of Engineering Technology (ET),Universiteit Twente,Technische Universiteit Eindhoven - Eindhoven University of Technology,Technische Universiteit Delft, Faculteit Civiele Techniek en Geowetenschappen, Materials, Mechanics, Management and Design (3MD), Applied Mechanics,Technische Universiteit Delft,Technische Universiteit Delft, Faculteit Civiele Techniek en Geowetenschappen,University of Warwick, Materials Engineering Centre (MEC),University of Warwick,Technische Universiteit Eindhoven - Eindhoven University of Technology,Universiteit Twente, Faculty of Engineering Technology (ET), Applied Mechanics & Data Analysis (AMDA),Technische Universiteit Delft, Faculteit Luchtvaart- en Ruimtevaarttechniek, Aerospace Manufacturing Technologies,Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Werktuigbouwkunde - Department of Mechanical Engineering, Mechanics of Materials,Technische Universiteit Delft, Faculteit Luchtvaart- en Ruimtevaarttechniek, Aerospace Structures & Materials,Universiteit Twente, Faculty of Engineering Technology (ET), Department of Mechanics of Solids, Surfaces & Systems (MS3), Production Technology,Universiteit Twente, Faculty of Science and Technology (TNW)Funder: Netherlands Organisation for Scientific Research (NWO) Project Code: P19-01-
more_vert assignment_turned_in ProjectFrom 2023Partners:Vrije Universiteit Amsterdam, Faculteit der Bètawetenschappen (Faculty of Science), Universiteit Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Computer Science, Formal Methods and Tools, Universiteit Twente, Universiteit Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Universiteit Twente +7 partnersVrije Universiteit Amsterdam, Faculteit der Bètawetenschappen (Faculty of Science),Universiteit Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Computer Science, Formal Methods and Tools,Universiteit Twente,Universiteit Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS),Universiteit Twente,Universiteit Twente, Faculty of Engineering Technology (ET), Applied Mechanics & Data Analysis (AMDA),Saxion,Vrije Universiteit Amsterdam, Faculteit der Bètawetenschappen (Faculty of Science), Afdeling Informatica (Computer Science), Artificial Intelligence,VU,Vrije Universiteit Amsterdam, Faculteit der Sociale Wetenschappen, Department of Computer Science,Universiteit Twente, Faculty of Engineering Technology (ET), Department of Mechanics of Solids, Surfaces & Systems (MS3),Universiteit Twente, Faculty of Engineering Technology (ET), Toegepaste Mechanica/werktuigbouwkundeFunder: Netherlands Organisation for Scientific Research (NWO) Project Code: KICH1.ST02.21.003No more system malfunctions? The ZORRO project is working on diagnostic methods for high-tech systems, such as MRI scanners and printers. By continuously monitoring their behaviour with suitable sensors, algorithms from AI can detect anomalous patterns and relate these to their root causes. Suitable measures, such as replacements or repairs, can then prevent failures. We aim at breakthroughs in complexity with ZORRO: not diagnostics for simple components, but for entire systems; efficient monitoring systems that combine different sensor types; automation of diagnostic processes by capturing domain knowledge in diagnostic models and integrate these into the engineering process for high-tech systems.
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