A software architecture based on Machine Learning (ML) and Finite Element Method (FEM) and aimed at improving the detection of damages in aircraft structure subjected to complex variable loadings is presented here. Firstly, the software relies on statistical tools used among others in fraud detection (One-Class Support Vector Machine, Local Outlier Factors, Isolation Forest, DBSCAN) to identify anomalies in a vast amount of data recorded over time by multiple strain gauges located on the structure of the aircraft. Once an anomaly is detected at a given time and for a specific set of strain gauges, it can be classified as insignificant or critical by the user. If the anomaly is critical, the data of the associated strain gauges can be used as input data for a FEM optimization. This static optimization allows to visually assess the position and geometry of possible cracks in the structure.