The 8th International Conference on System Reliability and Safety
Sicily, Italy - November 20-22, 2024

 

Olga Fink, EPFL, Switzerland

Olga Fink has been assistant professor of intelligent maintenance and operations systems at EPFL since March 2022.  Olga is also a research affiliate at Massachusetts Institute of Technology. Olga’s research focuses on Hybrid Algorithms Fusing Physics-Based Models and Deep Learning Algorithms, Hybrid Operational Digital Twins, Transfer Learning, Self-Supervised Learning, Deep Reinforcement Learning and Multi-Agent Systems for Intelligent Maintenance and Operations of Infrastructure and Complex Assets. Before joining EPFL faculty, Olga was assistant professor of intelligent maintenance systems at ETH Zurich from 2018 to 2022, being awarded the prestigious professorship grant of the Swiss National Science Foundation (SNSF). Between 2014 and 2018 she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW). Olga received her Ph.D. degree from ETH Zurich with the thesis on “Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems”, and Diploma degree in industrial engineering from Hamburg University of Technology. She has gained valuable industrial experience as reliability engineer with Stadler Bussnang AG and as reliability and maintenance expert with Pöyry Switzerland Ltd. In 2018, Olga was selected as one of the “Top 100 Women in Business, Switzerland” and in 2019, she was selected as young scientist of the World Economic Forum. In 2020 and 2021, she was  honored as young scientist of the World Laureate Forum.

 

Ming Zuo, University of Alberta, Canada

Dr. Mingjian Zuo is Full Professor of the University of Alberta, Canada and Founder and Chief Scientist of Mingserve Technology Co. Ltd., China. He received the Bachelor of Science degree in Agricultural Engineering in 1982 from Shandong Institute of Technology, China, and the Master of Science degree in 1986 and the Ph.D. degree in 1989 both in Industrial Engineering from Iowa State University, Ames, Iowa, USA. His research interests include system reliability analysis, maintenance modeling and optimization, signal processing, fault diagnosis, machine learning, and prognosis & health management. He is Fellow of the Canadian Academy of Engineering (CAE), Fellow of the Prognostics and Health Management Society (PHMS), Fellow of the Asia-Pacific Artificial Intelligence Association (FAAIA), Fellow of the Institute of Industrial and Systems Engineers (IISE), and Founding Fellow of the International Society of Engineering Asset Management (ISEAM). He served as Department Editor of IISE Transactions, Associate Editor of IEEE Transactions on Reliability, Associate Editor of Journal of Risk and Reliability, Associate Editor of International Journal of Quality, Reliability and Safety Engineering, Regional Editor of International Journal of Strategic Engineering Asset Management, and Editorial Board Member of Reliability Engineering and System Safety, Journal of Traffic and Transportation Engineering, and International Journal of Performability Engineering.

Speech Title: Machine Learning for Practical Prognosis and Health Management
Abstract: Machine learning has great potential for reliability assurance of engineering assets through prognosis and health management (PHM). It has been attracting attention from both academic and industrial sectors. Recent developments of machine learning, especially the evolving branches of deep learning, transfer learning, and reinforcement learning, bring new opportunities for effective PHM. This talk will first introduce general principles of prognosis and health management and machine learning. We will then present our recent research work on developing machine learning techniques for PHM. Finally, development of PHM tools for industrial settings including traditional and intelligent approaches will be covered.

Katrina Groth, University of Maryland, USA

Katrina M. Groth is an Associate Professor of Mechanical Engineering and the Director of the Reliability Engineering program at the University of Maryland. Groth specializes in safety, risk, and reliability analysis of energy systems. She has an active portfolio of research, including developing quantitative risk assessment (QRA) methods, prognostics and health management (PHM) techniques, and reliability data collection frameworks and algorithms. Her work has influenced safety practices and codes and standards for hydrogen fueling stations, hydrogen storage and electrolyzers, fuel cells, gas pipelines, and nuclear power plants and more. Groth has published over 125 peer-reviewed papers and technical reports, 1 textbook, and has developed multiple software packages. She has received numerous awards, including an NSF CAREER award in 2021 and a DOE Hydrogen Program R&D Award, and the David Okrent Award for Nuclear Safety. She holds a Ph.D. in Reliability Engineering from the University of Maryland.