Emerging Techniques for Reliability and Predictive Maintenance Empowering reliability and predictive maintenance with Digital Twins and AI
This Mini-Symposium aims to gather leading experts to discuss the implementation and implications of recent advances for reliability analysis and maintenance planning, specifically the integration of predictive maintenance, AI, digital twins and other related topics. There is much exciting new research on Digital Twins and AI that is making fundamental advancements in reliability and predictive maintenance. The increasing availability of condition-monitoring data has incentivized in recent years the development of machine learning for prognostics and diagnostics, big data analytics, generative AI. With these, Digital Twins have also become increasingly performant.
You are cordially invited to share your knowledge and opinions, and to learn more on the application of Digital Twins and/or AI models to support reliability and predictive maintenance.
Date and Time
Location
Hosts
Registration
- Date: 18 Nov 2024
- Time: 09:00 AM to 05:00 PM
- All times are (UTC+01:00) Paris
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- Contact Event Host
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Primary: France - Chair: benjamin.joguet@naval-group.com; Sweden and Norway - Chair: janet.lin@ltu.se
RS Contacts: (1) Toronto Chapter is Muthanna Jameel Al-Khishali muthanaj2005@gmail.com and co-chair Peng Dai daipengmay@gmail.com; (2) Ottawa Chapter & other Canada RaedAbdullah@ieee.org
- Starts 05 November 2024 12:00 AM
- Ends 18 November 2024 09:00 AM
- All times are (UTC+01:00) Paris
- No Admission Charge
Speakers
Anne Barros, Professor
Recent advancements in reliability and resilience at RRSC
Address:CentraleSupélec, , France
David Coit, Professor
Predictive maintenance at the intersection between OR and ML – methodological challenges and opportunities
Address:Rutgers University, , United States
Christophe Berenguer, Professor
On the degradation and RUL Control of Degrading Controlled Systems
Address:Grenoble Alps Univ., , France
Konstantinos C. Gryllias, Professor,
Fault diagnosis based on digital twins and transfer learning
Address:KU Leuven, , Belgium
Agenda
(Tentative)
8:30 – 9:00: Registration, Welcome coffee
9:00 – 9:30: Prof. Anne Barros, Centralesupélec France: Welcome speech; Recent advancements in reliability and resilience at RRSC, CentraleSupélec.
9:30 – 10:25: Prof. Zhiguo Zeng, Centralesupélec France: Empowering predictive maintenance with digital twins and AI: An application on robots.
10:25 – 10:35: Coffee break
10:35 – 11:30: Prof. Janet Lin, Lulea University of Technology, Sweden: Industrial AI-Driven Maintenance and the Evolution of Digital Twins
11:30 – 12:25: Prof. Konstantinos Gryllias, KU Leuven, Belgium: Fault diagnosis based on digital twins and transfer learning
12:25 – 13:30: Lunch
13:30 – 14:25: Prof. Giovanni Lugaresi, KU Leuven, Belgium: Data-Driven Modelling of Digital Twins for Circular Production Systems
14:25 – 15:20: Prof. Christophe Berenguer, Grenoble INP, France: On the degradation and RUL Control of Degrading Controlled Systems
15:20 – 15:30: Coffee break
15:30 – 16:25: Prof. Jie Liu, Beihang University, China: Causality-based representation learning for fault diagnosis of complex systems.
16:25 – 17:20: Prof. Mihaela Mitici, Utrecht University, the Netherlands and Prof. David Coit, Rutgers University, USA: Predictive maintenance at the intersection between OR and ML – methodological challenges and opportunities
17:20 – 17:45: Closing cocktail, announcement of next workshop, discussions.