PrimaVera Colloquium: Predictive Maintenance via Fault Tree Analysis and Model Checking

Speaker: Prof. dr. Marielle Stoelinga


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, Marielle will advocate a combination of model-driven (esp fault trees) and data analytical techniques to get more insight in the costs versus the system performance (in terms of availability, reliability, remaining useful lifetime) of maintenance strategies. She will show the results of three case studies from railroad engineering namely rail track (with Arcadis), the HVAC (heating, ventilation, airco; with NS).  
She will also go into recent developments on learning fault trees and rare event simulation. 


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 the 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 program leader of the Risk Management Master, a part-time MSc program 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.


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