Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification

TL;DR

This paper evaluates various signal processing and classification techniques, introducing a novel CBIR-based feature extraction method and demonstrating the effectiveness of U-BRAIN and neural networks for aerospace defect detection.

Contribution

It presents a new CBIR-based feature extraction method and compares soft computing techniques, highlighting the superior performance of the proposed approach in aerospace defect classification.

Findings

CBIR-based feature extraction outperforms other methods

U-BRAIN and neural networks show high effectiveness

Feature extraction is key to successful classification

Abstract

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. Keywords- Non-destructive testing (NDT); Soft Computing; Feature Extraction; Classification Algorithms; Content-Based Image Retrieval (CBIR); Eddy Currents (EC).