Condition monitoring with defect localisation in a two-dimensional structure based on linear discriminant and nearest neighbour classification of strain features

A method for condition monitoring and localization of defects in mass-produced structural members using supervised learning is presented. An example for the effectiveness of the developed method comprises cantilevered carbon composite plate. In a numerical finite element model, the plate is partitioned into zones and a point mass is put on several locations within each zone. Point mass is treated as a pseudo-defect locally modifying structural properties of the plate. For each act of mass application, strain values are recorded and serve as defect-sensitive feature. Two variables of classification are tested – two different supervised learning algorithms (linear discriminant and non-linear k-nearest neighbours) and a limited number of strain data points per class which is varied in the range of 2 to 9 points. Several query points are simulated and subjected to classification in terms of belonging to particular zones of the partitioned plate. This step can be treated as a defect localization. It is shown that only 2 strain readings per class are sufficient for defect localization. The methodology is experimentally validated on a cantilevered carbon composite prepreg of the same dimensions and properties.