Prof. MariĆ«lle Stoelinga – Predictive Maintenance via Fault Tree Analysis and Model Checking (May 27, 2020)

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.

Prof. Tiedo Tinga – Predictive maintenance, why and how? (May 13, 2020)

In this webinar a general introduction to (predictive) maintenance will be given. First the basic motivation for maintenance will be presented and an overview of various maintenance policies will be given. Then the more advanced policies, based on condition monitoring and prognostics will be discussed in more detail. The various options (model-based, data-driven) will be shown and the current status and challenges will be sketched. Finally, some case studies from our research projects will be used to demonstrate the potential and limitations.