The process of flying fighter jets naturally comes with tough environments and manoeu-vres where temperatures, pressures and forces all have a large impact on the aircraft. Part degeneration and general wear and tear greatly affects functionalities of the aircraft, and it is of importance to carefully monitor the well being of an aircraft in order to avoid catastrophic accidents. Therefore, this project aims to investigate various ways to improve anomaly detection of selected signals in the Gripen E fuel system. The methodology in this project was to compare collected flight data with generated data of a simulation model. The method was conducted for three selected signals with different properties, namely the transfer pump outlet pressure and flow, as well as the fuel mass in tank 2. A neural network was trained to generate predictions of the residual between measured and simulated flight data, together with a RandomForestRegressor to create a confidence interval of said signal. This made it possible to detect signal abnormalities when the gathered flight data heavily deviated from the generated machine learning algorithm predictions, thus alarming for anomalies. Investigated methods to improve anomaly detection includes feature selection, adding ar-tificial signals to facilitate machine learning algorithm training and filtering. A large part was also to see how an improved simulation model, and thus more accurate simulation data would affect the anomaly detection. A lot of effort was put into improving the simulation model, and investigating this area. In addition to this, the data balancing and features to balance the data on was revised. A significant challenge to tackle in this project was to map the modelling difficulties due to differences in signal properties. A by-productof improving the anomaly detection was that a general method was obtained to create a anomaly detection model of an arbitrarily chosen signal in the fuel system, regardless of the signal properties. Results show that the anomaly detection model was improved, with the main improvement area shown to be the choice of features. Improving the simulation model did not improve the anomaly detection in the transfer pump outlet pressure and flow, but it did however slightly facilitate anomaly detection of the fuel mass in tank 2 signal. It is also concluded that the signal properties can greatly affect the anomaly detection models, as accumulated effects in a signal can complicate anomaly detection. Remaining improvement areas such as filtering and addition of artificial signals can be helpful but needs to be looked into for each signal. It was also concluded that a stochastic behaviour was seen in the data balancing process, that could skew results if not handled properly. Over all the three selected signals, only one flight was misclassified as an anomaly, which can be seen as great results.