Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Sangmin Lee, Rajendra P. Palanisamy, Do-Kyung Pyun, Alp T. Findikoglu,
Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning,
Journal of Pipeline Science and Engineering,
2025,
100395,
ISSN 2667-1433,
https://doi.org/10.1016/j.jpse.2025.100395.
(https://www.sciencedirect.com/science/article/pii/S2667143325001428)
Abstract: Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.
Keywords: Pipe; deep learning; convolutional autoencoder; multi-mode wave; localization; NDT; SHM