Shape-based defect classification for Non Destructive Testing

TL;DR

This paper presents a novel shape-based method using impedance plane analysis and machine learning classifiers to detect and classify aerospace structure defects in eddy current non-destructive testing, demonstrating high accuracy and competitiveness.

Contribution

It introduces a new shape recognition approach based on impedance features combined with machine learning for defect classification in aerospace structures.

Findings

High accuracy in defect classification

Effective use of impedance shape features

Competitiveness against existing descriptors

Abstract

The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.