  Already before systems malfunction one has to know if hardware components
will fail in near future in order to counteract in time. Thus, unplanned
downtime is ought to be avoided. In medical imaging, maximizing the system's
uptime is crucial for patients' health and healthcare provider's daily
business. We aim to predict failures of Head/Neck coils used in Magnetic
Resonance Imaging (MRI) by training a statistical model on sequential data
collected over time. As image features depend on the coil's condition, their
deviations from the normal range already hint to future failure. Thus, we used
image features and their variation over time to predict coil damage. After
comparison of different time series classification methods we found Long Short
Term Memorys (LSTMs) to achieve the highest F-score of 86.43% and to tell with
98.33% accuracy if hardware should be replaced.
