Predictive Maintenance for Cyclotrons using Machine Learning

Abstract: A cyclotron is used for diagnosing and treating cancer. Pipes in the cyclotron have to be replaced as they get worn out when isotopes travel through them. This thesis aims to use machine learning models to predict when these parts have to be changed. Based on previous studies for predictive maintenance three dif- ferent machine learning models are used. The chosen models are random forest, gradient boosting and support vector machine. The results show that a gradient boosting regressor that predicts the number of remaining runs before the pipes have to be changed in the cyclotron is preferred. However, some data augmenta- tion had to be done to obtain these results, and future studies could explore the possibility of using a bigger data set or a multiple classifier approach.