The data throughput of the European XFEL DAQ is about 1.5 Gb/s. Data depicting the cavity signal behavior is currently only saved manually. This either happens, when cavity tests are being performed, or an operator detects a fault in the cavity system, that has to be further investigated. Those instances of interest are neither systematically nor automatically stored. It can therefore be assumed that unwanted or degraded cavity behavior is detected late or not at all. It is proposed to change the focus from detecting known faults (such as quenches) to additionally detect anomalies in the cavity system behavior. In order to detect anomalies in the cavity signals, an algorithm is proposed using a cavity model. It aims on finding those data sets, which diverge from the nominal cavity behavior, saving those instances for later analysis. The nominal behavior is defined by the cavity electromagnetic resonance model with beam loading as well as the model for the mechanical oscillations due to the Lorentz Forces. By using such an approach, the detection of anomalies, as well as faults could be automated. This contribution aims to summarize the influence of beam loading on the detection and gives examples for anomalies that were found in several cavities
