Tutorial 1
Predicting the Future with Confidence: A Tutorial on Uncertainty-Aware Prognostic
Prof. Dr. Marcos E. Orchard (Universidad de Chile, PHMS Fellow), Prof. Dr. Nick Eleftheroglou (iSP, TU Delft)
Outline:
Which fundamental property most clearly defines the essence of prognostics?
At its core, prognostics is the science of predicting the future. Yet the future, by definition, carries inherent uncertainty. Consequently, any prognostic approach must not only quantify and represent this uncertainty but also propagate it through time with efficiency and rigor.
Despite this fundamental requirement, many state‑of‑the‑art prognostic methods continue to neglect uncertainty. Their performance is often judged solely on accuracy, overlooking the critical importance of well‑calibrated uncertainty estimates—an element indispensable for meaningful and trustworthy decision‑making in real‑world operations.
In this tutorial, we lay out the foundations of uncertainty quantification in prognostics and present a practical framework for managing uncertainty. This framework enables predictions that are not only technically sound but also directly informative for operational planning and maintenance decision‑making.
We will showcase the value of uncertainty quantification and management through a live demonstration centered on electric vehicles, illustrating how uncertainty‑aware prognostics can optimize operational strategies. The demo will highlight how properly quantified and managed uncertainty provides distinct operational advantages and how the core principles of prognostics translate directly into enhanced real‑world performance.
The tutorial concludes with a discussion of open challenges and emerging research directions in the vital field of uncertainty‑aware prognostics, highlighting opportunities to advance decision‑making methodologies and real‑world applications.
Profiles:
Dr. Marcos Orchard (Google Scholar: h-index 42 and +7,200 citations) is Professor with the Department of Electrical Engineering of University of Chile, Director of the Center for Sustainable Acceleration of Electromobility (CASE), and Principal Investigator at the Advanced Center for Electrical and Electronic Engineering (Universidad Técnica Federico Santa María). He has authored and co-authored more than 200 papers on diverse topics, including the design and implementation of failure prognostic algorithms, statistical process monitoring, and system identification. His research work at the Georgia Institute of Technology was the foundation of novel real-time failure prognosis approaches based on particle filtering algorithms. His current research interests include the study of theoretical aspects related to the implementation of real-time failure prognosis algorithms and prognostic-decision-making, with applications to battery management systems, electromobility, mining industry, and finance. Prof. Orchard is Fellow of the Prognostic and Health Management Society, Editor-in-Chief of the International Journal of Prognostic and Health Management, and Past-President of the Chilean Association of Automatic Control (NMO of IFAC).
Dr. Nick Eleftheroglou is an Assistant Professor in the Faculty of Aerospace Engineering at Delft University of Technology and head of the Intelligent System Prognostics for Operations and Maintenance Group (https://www.groupisp.com/home), which currently consists of five PhD candidates, two postdoctoral researchers, as well as master’s students and visiting researchers. He also serves as an Associate Editor for the International Journal of Prognostics and Health Management. He received his Diploma in Mechanical and Aeronautics Engineering cum laude from the University of Patras, Greece, in 2015, and earned his PhD cum laude from TU Delft in October 2020. His research focuses on Prognostics and Health Management (PHM), with emphasis on the fundamentals of prognostics, such as uncertainty management, and on developing PHM methodologies that provide enhanced reliability, robustness, and feasibility for operations and maintenance.
Tutorial 2
To be announced.
Tutorial 3
To be announced.