Fault classification in electrofusion polyethylene joints by combined machine learning, thermal pulsing and IR thermography methods – A comparative study.

Highlights • A complementary fault classification stage for Thermal NDT-E of electrofusion PE joints. • Combined heat pulsing, IR imaging, and machine learning for classification accuracy. • The ovality diagnosis was more accurate than unalignment fault. • GLMNet (93.75%) was the best classifier, followed by Random Forests (84.21%) and k-means (70.37%). Abstract The capability of conveniently classifying the fault types in the electrofusion joints can certainly increase the security of polyethylene gas pipelines. Therefore in the current study, we use machine learning to push the horizons of our recent thermal pulsing and IR thermography method, to identify ovality versus unalignment faults. To do so, we extend our experimental IR-thermography data bank and then apply k -means, Random Forests and GLMNet algorithms in a two stage approach. The overall classification accuracy for k -means and Random Forests were 70.37% and 84.21% respectively; GLMNet could successfully outperform the others with a classification accuracy of 93.75%. [ABSTRACT FROM AUTHOR]