During the development of aerospace structures, typically many fatigue tests are conducted. During these tests, much effort is put into inspections in order to detect the onset of failure before complete failure. Strain sensor data may be used to reduce inspection effort. For this, a sufficient number of sensors need to be positioned appropriately to collect the relevant data. In order to minimize cost and effort associated with sensor positioning, the method proposed here aims at minimizing the number of necessary strain sensors while positioning them such that fatigue-induced damage can still be detected before complete failure. A suitable detection criterion is established as the relative change of strain amplitudes under cyclic loading. Then, the space of all possible crack lengths is explored. The regions where the detection criterion is satisfied before complete failure occurs are assembled into so-called detection zones. One sensor in this zone is sufficient to detect criticality. The applicability of the approach is demonstrated on a representative airplane structure that resembles a lower wing section. The method shows that four fatigue critical spots can be monitored using only one strain sensor in a non-intuitive position. Furthermore, we discuss two different strain measures for crack detection. The results of this paper can be used for reliable structural health monitoring using a minimum number of sensors.
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.
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