We propose a model to classify axial, radial loading profiles and rotation speed of wire-race bearings using Convolutional Neural Networks with spectrograms. Software-based models are able to measure quantities of machine elements. In many cases a physical sensor takes up a lot of space and is expensive. Wire-race bearings are lightweight bearings that can be installed in a space-saving manner. For predictive maintenance of the load-dependent lifetime estimation there is the need to record the stress conditions on the bearing. While force sensors have the ability to record these necessary stresses, they cannot be installed space-savingly. With the proposed setup using an accelerometer attached to the housing of a wire-race bearing, it is possible to both save space and reduce the number of physical sensors. Furthermore, we can show that the model is able to predict unseen data from 168 different load profiles with an overall accuracy of 86.3 %. In several experiments we generated vibration data which is processed using the fast Fourier transform algorithm and spectrograms. We show that image classification of spectrograms with Convolutional Neural Networks significantly outperforms other models with fast Fourier transforms fed into Artificial Neural Networks or logistic regression.