A fatigue crack quantification model for metallic structure based on strain monitoring data

Abstract

Obtaining the real-time fatigue crack length of a metallic structure is the prerequisite of the fatigue life monitoring and residual life estimation for an aircraft. This paper proposed a metallic structure's fatigue crack prediction model using strain monitoring data based on deep learning method. A cycle consistent adversarial network was developed to map the strain monitoring data from experimental measurement with those from finite element modeling. A crack size classification model and a crack length quantification model were proposed to classify the crack size range and identify the exact crack length, respectively. The proposed model was applied to predict the fatigue crack growth in centeral hole metallic plates subjected to random loading spectrum. The results showed that the prediction is effective and accurate.