Machine learning supported ultrasonic testing for characterization of cracks in polyethylene pipes
Said-El Hawwat, Jay Shah, Hao Wang
Abstract
• Ultrasonic guided wave testing supported by machine learning for characterization of cracks in PE pipes. • Investigated wave-crack interaction and determined damping properties of PE pipes through laboratory experiments. • Developed and validated finite element models with fine-tuned material properties for generation of a synthetic database. • Developed support vector machine (SVM) model for classification of crack severity with good accuracy. This study aims to develop a machine learning supported ultrasonic guided wave testing (UGWT) for detection and characterization of cracks in polyethylene (PE) pipes used for natural gas distribution. Ultrasonic testing is conducted to investigate the wave-crack interaction and determine damping properties of PE pipes. Finite element models are further built with material properties fine-tuned by wave attenuation experiments combined with cross-correlation analysis between the simulated and experimental signals. A synthetic database is populated using sensed signals over a wide range of crack geometries from numerical simulations. Finally, support vector machine (SVM) models are developed with different feature selections for classification of crack geometry and the accuracy is evaluated using both simulated and experimental cases. The findings demonstrate the potential of locating the circumferential position of crack with the ring-focusing method and classifying crack geometry in PE pipes using SVM model with central frequency features.