ArticleScientific reports2026
AI enabled pulse-echo based SHM system integrated with robotic gripper for the inspection of pipelines.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pipelines are essential infrastructure for moving fluids like water, oil, and gas over long distances. Early and reliable defect detection through non-destructive testing is essential to ensure structural integrity. The main purpose of this work is to propose an automated, AI-enabled pulse-echo ultrasonic structural health monitoring system integrated with a robotic gripper for non-destructive testing of circular pipes. The proposed system features a piezoelectric transducer mounted on a two-claw gripper, which is attached to a 6-DOF collaborative robot (CR5). This setup enables precise and repeatable scanning of steel, aluminum, and silver pipes with varying diameters and wall thicknesses. Experimental results demonstrate the successful excitation and reception of longitudinal guided wave modes, with clear sensitivity to pipe geometry, material properties, crack size (1 cm and 1.5 cm), and crack orientation ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]). Machine learning models, Random Forest, XGBoost, CatBoost, LightGBM, and Logistic Regression, are applied for the classification of defective pipe samples, assessing the percentage loss in the reflected echo’s amplitude, and to determine the crack localization on the surface of circular pipes.
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