Feature compensation and network reconstruction imaging with high-order helical modes in cylindrical waveguides

Abstract

Pipe wall loss assessment is crucial in oil and gas transportation. Ultrasonic guided wave is an effective technology to detect pipe defects. However, accurately inverting weak-feature defects under limited view conditions remains challenging due to constraints in transducer arrangements and inconsistent signal characteristics. This paper proposes a stepwise inversion method based on feature compensation and network reconstruction through deep learning, combined with high-order helical guided waves to expand the imaging view and achieve high-resolution imaging of pipe defects. A forward model was established using the finite difference method, with the two-dimensional Pearson correlation coefficient and maximum wall loss estimation accuracy defined as imaging metrics to evaluate and compare the method. Among 50 randomly selected defect samples in the test set, the inversion model achieved a correlation coefficient of 0.9669 and a maximum wall loss estimation accuracy of 96.65 %. Additionally, Gaussian noise was introduced to assess imaging robustness under pure signal, 5 dB, and 3 dB conditions. Laboratory experiments validated the practical feasibility of the proposed method. This approach is generalizable and holds significant potential for nondestructive testing in cylindrical waveguide structures represented by pipes.