Maintenance Meets Model Checking: Predictive Maintenance via Fault Trees and Formal Methods  

14 October 2022 | Online | 15:30 | Mariëlle Stoelinga (Radboud University Nijmegen and the University of Twente)  

Abstract
Proper maintenance is crucial to keep our trains, power plants and robots up and running. Since maintenance is also expensive, effective maintenance is a typical optimization problem, where one balances costs against system performance (in terms of availability, reliability, remaining useful lifetime)

Predictive maintenance is a promising technique that aims at predicting failures more accurately, so that just-in-time maintenance can be performed, doing maintenance exactly when and where needed. Thus, predictive maintenance promises higher availability, fewer failures at lower costs. In this talk, I will advocate a combination of model-driven (esp fault trees) and data analytical techniques to get more insight in the costs versus performance of maintenance strategies. I will show the results of several case studies from railroad engineering.

 I will also go into recent developments on learning fault trees and rare event simulation.


Bio
Prof. Dr. Mariëlle Stoelinga is a professor of risk management, both at the Radboud University Nijmegen, and the University of Twente, in the Netherlands.Stoelinga is the project coordinator on PrimaVera, a large collaborative project on Predictive Maintenance in the Dutch National Science Agenda NWA. She also received a prestigious ERC consolidator grant  Stoelinga is the scientific programme leader  Risk Management Master, a part-time MSc programme for professionals. She holds an MSc and a PhD degree from Radboud University Nijmegen, and has spent several years as a post-doc at the University of California at Santa Cruz, USA.