For robotic systems involved in challenging environments it is crucial to be able to 2 identify faults as early as possible. In challenging environments it is not always possible to explore 3 all of the fault space, thus anomalous data can act as a broader surrogate, where a anomaly 4 may represent a fault or a predecessor to a fault. This paper proposes a method for identifying 5 anomalous data from a robot, whilst using minimal nominal data for training. A Monte-Carlo 6 ensemble sampled Variational Autoencoder is utilised to determine nominal and anomalous 7 data through reconstructing live data. This has been tested on simulated anomalies on real data, 8 demonstrating the technique being capable of reliable identifying anomaly, with no pre-knowledge 9 of the system. With the proposed system getting an F1-score of 0.85 in testing.