SISO collected T48 probe data from an aeroderivative engine across multiple runs to develop an anomaly detection algorithm. The process involved data collection, cleaning, and training a neural network. The trained model was then validated against a separate dataset containing both normal and faulty conditions to assess error detection performance. If the algorithm met accuracy requirements, it was deemed suitable for future monitoring; otherwise, it was retrained with adjusted inputs.