Tracking Accelerated Aging of Cross-Linked Polyethylene Pipes by Applying Machine Learning Concepts to Infrared Spectra

Cross-linked polyethylene (PEX) pipes are promising replacements for metal or concrete pipes used for water, gas and sewage transport. Characterizing changes to the polymer and additive compounds with in-service use is paramount to predicting pipe failure. Infrared (IR) microscopy combines the chemical specificity of IR spectroscopy with the high spatial resolution of light microscopy, and we have used this technique to track variations in the degree of crystallinity and additive concentration across the wall thickness of PEX pipes. We have shown that principal component analysis of IR absorbance peaks can be used to differentiate and classify different pipe formulations [1]. We have used this methodology to characterize changes to pipes that have been subjected to accelerated aging involving heating in water and air, and exposure to ultraviolet radiation. This has allowed us to identify and track IR peaks that are most relevant to pipe degradation. We have used these results, together with machine learning techniques, to identify and classify different modes of pipe degradation.

[1] M. Hiles et al., Classifying Formulations of Crosslinked Polyethylene Pipe by Applying Machine-Learning Concepts to Infrared Spectra, J. Polym. Sci. Pol. Phys. 57, 1255–1262 (2019).