Abstract In recent years, the health condition of assets is often monitored by sensors. The obtained condition-monitoring data has incentivized the development of data-driven Remaining-Useful-Life (RUL) prognostics. These RUL prognostics are further informing maintenance planning (predictive maintenance). In this presentation I will discuss the case of predictive maintenance for aircraft engines. Using Convolutional Neural Networks, probabilistic RUL prognostics are developed. These prognostics are the basis to identifying optimal maintenance times, following the theory of renewal reward processes. The results show that this predictive maintenance model leads to low maintenance costs and number of failures, without wasting the life of the assets. Overall, the proposed framework provides a roadmap from sensor measurements to data-driven prognostics to predictive maintenance planning.

mobility. Her work has been awarded Best Paper Award 2nd prize at the 2022 Prognostics and Health Management Europe (PHMe) Conference, Thomas L. Fagan Award at the 2021 Reliability and Maintainability Symposium (RAMS), and Best Innovation Award at the 2021 AGIFORS Aircraft Maintenance Operations Special Session.