  In order to ensure trouble-free operation, prediction of hardware failures is
essential. This applies especially to medical systems. Our goal is to determine
hardware which needs to be exchanged before failing. In this work, we focus on
predicting failures of 20-channel Head/Neck coils using image-related
measurements. Thus, we aim to solve a classification problem with two classes,
normal and broken coil. To solve this problem, we use data of two different
levels. One level refers to one-dimensional features per individual coil
channel on which we found a fully connected neural network to perform best. The
other data level uses matrices which represent the overall coil condition and
feeds a different neural network. We stack the predictions of those two
networks and train a Random Forest classifier as the ensemble learner. Thus,
combining insights of both trained models improves the prediction results and
allows us to determine the coil's condition with an F-score of 94.14% and an
accuracy of 99.09%.
