Feature extraction and soft computing methods for aerospace structure defect classification

• We apply feature extraction methods and soft computing techniques in aerospace defect classification.
• The key to a successful defect classifier is the feature extraction method.
• A novel CBIR-based feature extraction method outperforms all the competitors.
• The U-BRAIN algorithm and the MLP neural network are the most effective classifier.

This study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one; and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method – namely the novel CBIR-based one outperforms all the competitors – and they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application.

Figure optionsDownload as PowerPoint slide