Predictive maintenance of a rotating condenser inside a synchrocyclotron

Hamaide, Valentin [UCL] Glineur, François [UCL]

This paper investigates data-driven methods to predict failures of a rotating condenser (RotCo) inside a synchrocyclotron for a proton therapy treatment system [12]. Downtime caused by a failure of the system can lead to significant delays in the treatment of the patients, which is why having a reliable predictive maintenance system is essential. The condenser rotates at high speed and rolling bearing elements are responsible for maintaining low friction between the moving components. The aim is to predict failures of the bearing box which contains the shaft and the bearing elements. Several sensors within the cyclotron are constantly measuring multiple relevant signals but, notably, vibration data is not available. We leverage those time-series data to predict a few days in advance whether a failure is likely to happen. To do this, we propose a two-level approach that relies on combining the output of a classifier with an aggregator based on a custom business metric specifically designed for this problem.