The worldwide effort on fusion research aims to realize a means of producing clean and safe energy for future generations. At the JET tokamak, extensive research is being performed to help accomplish this goal, but as with all complex machinery, component failures occur. In this work, two failure cases at JET are addressed with the goal of predictive maintenance by means of anomaly detection and other machine learning techniques.