2025/07/03 by Tikesh Kumar Sahu, S. Thirunavukkarasu, Anish Kumar
Engineering · #Artificial intelligence #Composite material #Computer science #Current (fluid) #Eddy current #Eddy-current testing #Electrical engineering #Engineering #Machine learning #Materials science #Non-Destructive Testing Techniques #Nondestructive testing #Pattern recognition (psychology) #Physics #Solid mechanics #Ultrasonics and Acoustic Wave Propagation #Welding Techniques and Residual Stresses
paper · pdf · doi:10.1007/s10921-025-01229-2
crossref issued 2025/07/03 · crossref published 2025/07/03 · crossref published-online 2025/07/03 · openalex publication_date 2025/07/03 · crossref created 2025/07/03 · crossref published-print 2025/09/01 · crossref deposited 2025/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23 · crossref indexed 2026/08/05
The paper presents a robust machine learning model for automated classification of flaw signals from eddy current inspection data of heat exchanger tubes. The proposed model employs four sliding window based ingenious features namely variance, template correlation, template dynamic time warping distance and area under the signal with Random Forest supervised machine learning model, to identify flaws. The efficacy of the model is evaluated on tube inspection data acquired in a heat exchanger by comparing its performance against expert analysis. The machine learning model exhibits an impressive accuracy of 99.94% for classification of flaw signals in addition to higher desirable metrics such as precision, recall, F1-score and Matthews correlation coefficient (MCC). This work lays a strong foundation for developing a real-time, robust and reliable flaw detection system.