  Numerical Relativity has been of fundamental importance for studying compact
binary coalescence dynamics, waveform modelling, and eventually for
gravitational waves observations. As the sensitivity of the detector network
improves, more precise template modelling will be necessary to guarantee a more
accurate estimation of astrophysical parameters. To help improve the accuracy
of numerical relativity catalogs, we developed a deep learning model capable of
detecting anomalous waveforms. We analyzed 1341 binary black hole simulations
from the SXS catalog with various mass-ratios and spins, considering waveform
dominant and higher modes. In the set of waveform analyzed, we found and
categorised seven types of anomalies appearing in the coalescence phases.
