Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection

Abstract

Infrared thermography is a non-destructive testing method used to detect defects in materials and structures. Machine learning algorithms have been applied to thermographic data to automate the defect detection process. Data preparation and feature extraction are crucial factors affecting ML model results, especially in thermographic data analysis. This study focuses on automating the detection of impact damage in carbon fiber-reinforced polymer materials using flash-pulse thermography and ML algorithms. Various machine learning models and data pre-processing techniques were evaluated for their effectiveness in detecting and locating impact damage. The results demonstrated that the combination of the K-nearest neighbors model with the differential absolute contrast data processing method achieved the highest balanced accuracy. Other combinations, such as Gaussian support vector machine model with raw data and K-nearest neighbor with thermographic signal reconstruction derivative data, also exhibited promising performances.