Evaluation of Machine Learning Algorithms for Flaw Detection from Eddy Current Testing Waveform Data in Heat Exchangers

Abstract

Heat exchangers play a critical role in plants and industrial facilities, and effective flaw detection techniques are essential to maintain their integrity. This study aims to enhance flaw detection efficiency by analyzing waveform data obtained from eddy current testing using machine learning algorithms. Data preprocessing and feature extraction were performed, followed by a comparison of multiple algorithms, including Support Vector Machines, Random Forests, and Neural Networks. The results revealed variations in detection accuracy based on flaw types and locations, enabling the identification of highly accurate and reliable models. These findings are expected to improve the efficiency and reliability of non-destructive testing for heat exchangers.