Safety in nuclear power plants is very important and the capability of detecting possible anomalies and perturbations through computational models is encouraged by the regulatory authorities as a complementary tool in safety studies and safety assessment. Anomaly detection provides early warning of faults, by identifying deviations in behavior between real-time data from the system and the expected values produced by a predictive model. In the present work, two different methods have been implemented for the detection and isolation of outliers: the Autoencoder Method and a density estimation method, using Finite Mixture Model. In the former, a sparse autoencoder is trained with normal transactions and it will learn how to represent a normal input data.