Predictive Maintenance via Fault Tree Analysis and Model Checking
by Prof. dr. Mariëlle Stoelinga
27th May 2020
About Prof. dr. Mariëlle Stoelinga
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.
Abstract
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 the system performance (in terms of availability, reliability, remaining useful lifetime) of maintenance strategies. I will show the results of three case studies from railroad engineering namely rail track (with Arcadis), the HVAC (heating, ventilation, airco; with NS). I will also go into recent developments on learning fault trees and rare event simulation.
The slides are available here.